From fd2ceaba7356b6e04f4bd8bfa82cfbc92e4f016a Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Wed, 2 Aug 2023 21:44:59 -0400
Subject: [PATCH 1/7] add my script

---
 Pipfile      |   1 +
 Pipfile.lock | 354 ++++++++++++++++++++++-----------------------------
 script.py    | 205 +++++++++++++++++++++++++++++
 3 files changed, 355 insertions(+), 205 deletions(-)
 create mode 100644 script.py

diff --git a/Pipfile b/Pipfile
index aef7b466..7dfbb58d 100644
--- a/Pipfile
+++ b/Pipfile
@@ -15,6 +15,7 @@ scikit-learn = "*"
 mapclassify = "*"
 black = "*"
 fiona = "*"
+esridump = "*"
 
 [dev-packages]
 
diff --git a/Pipfile.lock b/Pipfile.lock
index 34d157b9..7883cc7a 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "56fef17634b2e1a6d5d5779ddb0eac1f86a0c153ed0848d2d2e669f1990ad4cb"
+            "sha256": "b1e540e2ca2141f8538cc433316660fac0aa0a767b4327edec27fbe92da97600"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -33,6 +33,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.7.1"
         },
+        "appnope": {
+            "hashes": [
+                "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24",
+                "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"
+            ],
+            "markers": "platform_system == 'Darwin'",
+            "version": "==0.1.3"
+        },
         "argon2-cffi": {
             "hashes": [
                 "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80",
@@ -85,11 +93,11 @@
         },
         "async-lru": {
             "hashes": [
-                "sha256:00c0a8899c20b9c88663a47732689ff98189c9fa08ad9f734d7722f934d250b1",
-                "sha256:b714c9d1415fca4e264da72a9e2abc66880ce7430e03a973341f88ea4c0d4869"
+                "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627",
+                "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.0.3"
+            "version": "==2.0.4"
         },
         "attrs": {
             "hashes": [
@@ -339,21 +347,13 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3' and python_version < '4'",
             "version": "==0.7.2"
         },
-        "colorama": {
-            "hashes": [
-                "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44",
-                "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"
-            ],
-            "markers": "platform_system == 'Windows'",
-            "version": "==0.4.6"
-        },
         "comm": {
             "hashes": [
-                "sha256:16613c6211e20223f215fc6d3b266a247b6e2641bf4e0a3ad34cb1aff2aa3f37",
-                "sha256:a61efa9daffcfbe66fd643ba966f846a624e4e6d6767eda9cf6e993aadaab93e"
+                "sha256:354e40a59c9dd6db50c5cc6b4acc887d82e9603787f83b68c01a80a923984d15",
+                "sha256:6d52794cba11b36ed9860999cd10fd02d6b2eac177068fdd585e1e2f8a96e67a"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==0.1.3"
+            "version": "==0.1.4"
         },
         "contourpy": {
             "hashes": [
@@ -410,27 +410,27 @@
         },
         "debugpy": {
             "hashes": [
-                "sha256:0679b7e1e3523bd7d7869447ec67b59728675aadfc038550a63a362b63029d2c",
-                "sha256:279d64c408c60431c8ee832dfd9ace7c396984fd7341fa3116aee414e7dcd88d",
-                "sha256:33edb4afa85c098c24cc361d72ba7c21bb92f501104514d4ffec1fb36e09c01a",
-                "sha256:38ed626353e7c63f4b11efad659be04c23de2b0d15efff77b60e4740ea685d07",
-                "sha256:5224eabbbeddcf1943d4e2821876f3e5d7d383f27390b82da5d9558fd4eb30a9",
-                "sha256:53f7a456bc50706a0eaabecf2d3ce44c4d5010e46dfc65b6b81a518b42866267",
-                "sha256:9cd10cf338e0907fdcf9eac9087faa30f150ef5445af5a545d307055141dd7a4",
-                "sha256:aaf6da50377ff4056c8ed470da24632b42e4087bc826845daad7af211e00faad",
-                "sha256:b3e7ac809b991006ad7f857f016fa92014445085711ef111fdc3f74f66144096",
-                "sha256:bae1123dff5bfe548ba1683eb972329ba6d646c3a80e6b4c06cd1b1dd0205e9b",
-                "sha256:c0ff93ae90a03b06d85b2c529eca51ab15457868a377c4cc40a23ab0e4e552a3",
-                "sha256:c4c2f0810fa25323abfdfa36cbbbb24e5c3b1a42cb762782de64439c575d67f2",
-                "sha256:d71b31117779d9a90b745720c0eab54ae1da76d5b38c8026c654f4a066b0130a",
-                "sha256:dbe04e7568aa69361a5b4c47b4493d5680bfa3a911d1e105fbea1b1f23f3eb45",
-                "sha256:de86029696e1b3b4d0d49076b9eba606c226e33ae312a57a46dca14ff370894d",
-                "sha256:e3876611d114a18aafef6383695dfc3f1217c98a9168c1aaf1a02b01ec7d8d1e",
-                "sha256:ed6d5413474e209ba50b1a75b2d9eecf64d41e6e4501977991cdc755dc83ab0f",
-                "sha256:f90a2d4ad9a035cee7331c06a4cf2245e38bd7c89554fe3b616d90ab8aab89cc"
+                "sha256:05d1b288167ce3bfc8e1912ebed036207a27b9569ae4476f18287902501689c6",
+                "sha256:1ca76d3ebb0e6368e107cf2e005e848d3c7705a5b513fdf65470a6f4e49a2de7",
+                "sha256:1fe3baa28f5a14d8d2a60dded9ea088e27b33f1854ae9a0a1faa1ba03a8b7e47",
+                "sha256:2345beced3e79fd8ac4158e839a1604d5cccd19beb45561a1ffe2e5b33465f28",
+                "sha256:3b7091d908dec70022b8966c32b1e9eaf183ff05291edf1d147fee153f4cb9f8",
+                "sha256:406b3a6cb7548d73260f69a511178ec9196779cafda68e563488c6f94cc88670",
+                "sha256:4172383b961a2334d29168c7f7b24f2f99d29291a945016986c78a5683fba915",
+                "sha256:5502e14de6b7241ecf7c4fa4ec6dd61d0824da7a09020c7ffe7be4cd09d36f24",
+                "sha256:591aac0e69bc75102d9f9294f1228e5d9ff9aa17b8c88e48b1bbb3dab8a54dcc",
+                "sha256:6830947f68b41cd6abe20941ec3303a8452c40ff5fe3637c6efe233e395ecebc",
+                "sha256:6ca1c92e30e2aaeca156d5bd76e1587c23e332474a7b12e1900dd632b31ce05e",
+                "sha256:8c1f5a3286fb633f691c594649e9d2e8e30292c9eaf49e38d7da525151b33a83",
+                "sha256:959f9b8181a4c544b067daff8d881cd3ac4c7aec1a3a4f41f81c529795b3d864",
+                "sha256:95f7ce92450b72abcf0c479539a7d00c20e68f1f1fb447eef0b08d2a635d96d7",
+                "sha256:bb27b8e08f8e60705de6cf05b5da4c21e5a0bc2ca73f06fc36646f456df18ff5",
+                "sha256:f16bb157b6018ce6a23b64653a6b1892f046cc2b0576df1794c6b22f9fd82118",
+                "sha256:f4a7193cec3f1e188963f6e8699e1187f758a0a4bbce511b3ad40caf618fc888",
+                "sha256:f7a80c50b89d8fb49c9e5b6ee28c0bfb822fbd33fef0f2f9843724d0d1984e4e"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.6.7"
+            "version": "==1.6.8"
         },
         "decorator": {
             "hashes": [
@@ -448,13 +448,13 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
             "version": "==0.7.1"
         },
-        "exceptiongroup": {
+        "esridump": {
             "hashes": [
-                "sha256:12c3e887d6485d16943a309616de20ae5582633e0a2eda17f4e10fd61c1e8af5",
-                "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"
+                "sha256:3ea0b856da1a55b327375fb1eec24d08ec2a5eeda8700a9e696159252876dca7",
+                "sha256:bfda87d3b9fc55dcfe1c1df583c98a7117f3861c02443e1350f77ffcca2237a2"
             ],
-            "markers": "python_version < '3.11'",
-            "version": "==1.1.2"
+            "index": "pypi",
+            "version": "==1.13.0"
         },
         "executing": {
             "hashes": [
@@ -498,43 +498,43 @@
         },
         "fonttools": {
             "hashes": [
-                "sha256:1df1b6f4c7c4bc8201eb47f3b268adbf2539943aa43c400f84556557e3e109c0",
-                "sha256:2a22b2c425c698dcd5d6b0ff0b566e8e9663172118db6fd5f1941f9b8063da9b",
-                "sha256:33191f062549e6bb1a4782c22a04ebd37009c09360e2d6686ac5083774d06d95",
-                "sha256:38cdecd8f1fd4bf4daae7fed1b3170dfc1b523388d6664b2204b351820aa78a7",
-                "sha256:3ae64303ba670f8959fdaaa30ba0c2dabe75364fdec1caeee596c45d51ca3425",
-                "sha256:3d1f9471134affc1e3b1b806db6e3e2ad3fa99439e332f1881a474c825101096",
-                "sha256:4e3334d51f0e37e2c6056e67141b2adabc92613a968797e2571ca8a03bd64773",
-                "sha256:4edc795533421e98f60acee7d28fc8d941ff5ac10f44668c9c3635ad72ae9045",
-                "sha256:547ab36a799dded58a46fa647266c24d0ed43a66028cd1cd4370b246ad426cac",
-                "sha256:59eba8b2e749a1de85760da22333f3d17c42b66e03758855a12a2a542723c6e7",
-                "sha256:704bccd69b0abb6fab9f5e4d2b75896afa48b427caa2c7988792a2ffce35b441",
-                "sha256:73ef0bb5d60eb02ba4d3a7d23ada32184bd86007cb2de3657cfcb1175325fc83",
-                "sha256:7763316111df7b5165529f4183a334aa24c13cdb5375ffa1dc8ce309c8bf4e5c",
-                "sha256:849ec722bbf7d3501a0e879e57dec1fc54919d31bff3f690af30bb87970f9784",
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-                "sha256:f48602c0b3fd79cd83a34c40af565fe6db7ac9085c8823b552e6e751e3a5b8be"
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+                "sha256:48e82d776d2e93f88ca56567509d102266e7ab2fb707a0326f032fe657335238",
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+                "sha256:58c1165f9b2662645de9b19a8c8bdd636b36294ccc07e1b0163856b74f10bafc",
+                "sha256:614b1283dca88effd20ee48160518e6de275ce9b5456a3134d5f235523fc5065",
+                "sha256:685a4dd6cf31593b50d6d441feb7781a4a7ef61e19551463e14ed7c527b86f9f",
+                "sha256:6bd7e4777bff1dcb7c4eff4786998422770f3bfbef8be401c5332895517ba3fa",
+                "sha256:703101eb0490fae32baf385385d47787b73d9ea55253df43b487c89ec767e0d7",
+                "sha256:83b98be5d291e08501bd4fc0c4e0f8e6e05b99f3924068b17c5c9972af6fff84",
+                "sha256:8ece1886d12bb36c48c00b2031518877f41abae317e3a55620d38e307d799b7e",
+                "sha256:9c456d1f23deff64ffc8b5b098718e149279abdea4d8692dba69172fb6a0d597",
+                "sha256:9cd2363ea7728496827658682d049ffb2e98525e2247ca64554864a8cc945568",
+                "sha256:a9b55d2a3b360e0c7fc5bd8badf1503ca1c11dd3a1cd20f2c26787ffa145a9c7",
+                "sha256:ae7df0ae9ee2f3f7676b0ff6f4ebe48ad0acaeeeaa0b6839d15dbf0709f2c5ef",
+                "sha256:ae881e484702efdb6cf756462622de81d4414c454edfd950b137e9a7352b3cb9",
+                "sha256:b8600ae7dce6ec3ddfb201abb98c9d53abbf8064d7ac0c8a0d8925e722ccf2a0",
+                "sha256:c36c904ce0322df01e590ba814d5d69e084e985d7e4c2869378671d79662a7d4",
+                "sha256:c8bf88f9e3ce347c716921804ef3a8330cb128284eb6c0b6c4b3574f3c580023",
+                "sha256:d40673b2e927f7cd0819c6f04489dfbeb337b4a7b10fc633c89bf4f34ecb9620",
+                "sha256:d54e600a2bcfa5cdaa860237765c01804a03b08404d6affcd92942fa7315ffba",
+                "sha256:dfe7fa7e607f7e8b58d0c32501a3a7cac148538300626d1b930082c90ae7f6bd",
+                "sha256:e35bed436726194c5e6e094fdfb423fb7afaa0211199f9d245e59e11118c576c",
+                "sha256:f0290ea7f9945174bd4dfd66e96149037441eb2008f3649094f056201d99e293",
+                "sha256:fae4e801b774cc62cecf4a57b1eae4097903fced00c608d9e2bc8f84cd87b54a"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.41.1"
+            "version": "==4.42.0"
         },
         "fqdn": {
             "hashes": [
@@ -559,29 +559,13 @@
             "markers": "python_version >= '3.5'",
             "version": "==3.4"
         },
-        "importlib-metadata": {
-            "hashes": [
-                "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb",
-                "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"
-            ],
-            "markers": "python_version < '3.10'",
-            "version": "==6.8.0"
-        },
-        "importlib-resources": {
-            "hashes": [
-                "sha256:4cf94875a8368bd89531a756df9a9ebe1f150e0f885030b461237bc7f2d905f2",
-                "sha256:d952faee11004c045f785bb5636e8f885bed30dc3c940d5d42798a2a4541c185"
-            ],
-            "markers": "python_version < '3.10'",
-            "version": "==6.0.0"
-        },
         "ipykernel": {
             "hashes": [
-                "sha256:29cea0a716b1176d002a61d0b0c851f34536495bc4ef7dd0222c88b41b816123",
-                "sha256:2f5fffc7ad8f1fd5aadb4e171ba9129d9668dbafa374732cf9511ada52d6547f"
+                "sha256:e342ce84712861be4b248c4a73472be4702c1b0dd77448bfd6bcfb3af9d5ddf9",
+                "sha256:f0042e867ac3f6bca1679e6a88cbd6a58ed93a44f9d0866aecde6efe8de76659"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==6.24.0"
+            "version": "==6.25.0"
         },
         "ipython": {
             "hashes": [
@@ -600,11 +584,11 @@
         },
         "ipywidgets": {
             "hashes": [
-                "sha256:50ace0a8886e9a0d68b980db82f94c25d55d21ff2340ed36f802dd9365e94acf",
-                "sha256:e0aed0c95a1e55b6a123f64305245578bdc09e52965a34941c2b6a578b8c64a0"
+                "sha256:6c8396cc7b8c95dfb4e9ab0054f48c002f045e7e5d7ae523f559d64e525a98ab",
+                "sha256:ce97dd90525b3066fd00094690964e7eac14cf9b7745d35565b5eeac20cce687"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==8.0.7"
+            "version": "==8.1.0"
         },
         "isoduration": {
             "hashes": [
@@ -615,11 +599,11 @@
         },
         "jedi": {
             "hashes": [
-                "sha256:203c1fd9d969ab8f2119ec0a3342e0b49910045abe6af0a3ae83a5764d54639e",
-                "sha256:bae794c30d07f6d910d32a7048af09b5a39ed740918da923c6b780790ebac612"
+                "sha256:bcf9894f1753969cbac8022a8c2eaee06bfa3724e4192470aaffe7eb6272b0c4",
+                "sha256:cb8ce23fbccff0025e9386b5cf85e892f94c9b822378f8da49970471335ac64e"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==0.18.2"
+            "version": "==0.19.0"
         },
         "jinja2": {
             "hashes": [
@@ -653,11 +637,11 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:971be834317c22daaa9132340a51c01b50910724082c2c1a2ac87eeec153a3fe",
-                "sha256:fb3642735399fa958c0d2aad7057901554596c63349f4f6b283c493cf692a25d"
+                "sha256:ce71d2f8c7983ef75a756e568317bf54bc531dc3ad7e66a128eae0d51623d8a3",
+                "sha256:dc274409c36175aad949c68e5ead0853aaffbe8e88c830ae66bb3c7a1728ad2d"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.18.4"
+            "version": "==4.18.6"
         },
         "jsonschema-specifications": {
             "hashes": [
@@ -702,11 +686,11 @@
         },
         "jupyter-events": {
             "hashes": [
-                "sha256:57a2749f87ba387cd1bfd9b22a0875b889237dbf2edc2121ebb22bde47036c17",
-                "sha256:9a6e9995f75d1b7146b436ea24d696ce3a35bfa8bfe45e0c33c334c79464d0b3"
+                "sha256:4753da434c13a37c3f3c89b500afa0c0a6241633441421f6adafe2fb2e2b924e",
+                "sha256:7be27f54b8388c03eefea123a4f79247c5b9381c49fb1cd48615ee191eb12615"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.6.3"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.7.0"
         },
         "jupyter-lsp": {
             "hashes": [
@@ -983,27 +967,27 @@
         },
         "nbconvert": {
             "hashes": [
-                "sha256:25e0cf2b663ee0cd5a90afb6b2f2940bf1abe5cc5bc995b88c8156ca65fa7ede",
-                "sha256:36d3e7bf32f0c075878176cdeeb645931c994cbed5b747bc7a570ba8cd2321f3"
+                "sha256:3022adadff3f86578a47fab7c2228bb3ca9c56a24345642a22f917f6168b48fc",
+                "sha256:4a5996bf5f3cd16aa0431897ba1aa4c64842c2079f434b3dc6b8c4b252ef3355"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==7.7.2"
+            "version": "==7.7.3"
         },
         "nbformat": {
             "hashes": [
-                "sha256:3a7f52d040639cbd8a3890218c8b0ffb93211588c57446c90095e32ba5881b5d",
-                "sha256:b7968ebf4811178a4108ee837eae1442e3f054132100f0359219e9ed1ce3ca45"
+                "sha256:1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9",
+                "sha256:5f98b5ba1997dff175e77e0c17d5c10a96eaed2cbd1de3533d1fc35d5e111192"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==5.9.1"
+            "version": "==5.9.2"
         },
         "nest-asyncio": {
             "hashes": [
-                "sha256:b9a953fb40dceaa587d109609098db21900182b16440652454a146cffb06e8b8",
-                "sha256:d267cc1ff794403f7df692964d1d2a3fa9418ffea2a3f6859a439ff482fef290"
+                "sha256:5301c82941b550b3123a1ea772ba9a1c80bad3a182be8c1a5ae6ad3be57a9657",
+                "sha256:6a80f7b98f24d9083ed24608977c09dd608d83f91cccc24c9d2cba6d10e01c10"
             ],
             "markers": "python_version >= '3.5'",
-            "version": "==1.5.6"
+            "version": "==1.5.7"
         },
         "networkx": {
             "hashes": [
@@ -1015,11 +999,11 @@
         },
         "notebook": {
             "hashes": [
-                "sha256:38b55e6939df0ba73b53212c3b234e41102f1789e0158606cedaebf00abef6c8",
-                "sha256:71b4e695e658763a2766613176491854708fb46fbe7664bf5e494deeeab92d60"
+                "sha256:2e16ad4e63ea89f7efbe212ee7c1693fcfa5ab55ffef75047530f74af4bd926c",
+                "sha256:35327476042140e8739ff8fcfecdc915658ae72b4db72d6e3b537badcdbf9e35"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==7.0.0"
+            "version": "==7.0.1"
         },
         "notebook-shim": {
             "hashes": [
@@ -1031,34 +1015,34 @@
         },
         "numpy": {
             "hashes": [
-                "sha256:012097b5b0d00a11070e8f2e261128c44157a8689f7dedcf35576e525893f4fe",
-                "sha256:0d3fe3dd0506a28493d82dc3cf254be8cd0d26f4008a417385cbf1ae95b54004",
-                "sha256:0def91f8af6ec4bb94c370e38c575855bf1d0be8a8fbfba42ef9c073faf2cf19",
-                "sha256:1a180429394f81c7933634ae49b37b472d343cccb5bb0c4a575ac8bbc433722f",
-                "sha256:1d5d3c68e443c90b38fdf8ef40e60e2538a27548b39b12b73132456847f4b631",
-                "sha256:20e1266411120a4f16fad8efa8e0454d21d00b8c7cee5b5ccad7565d95eb42dd",
-                "sha256:247d3ffdd7775bdf191f848be8d49100495114c82c2bd134e8d5d075fb386a1c",
-                "sha256:35a9527c977b924042170a0887de727cd84ff179e478481404c5dc66b4170009",
-                "sha256:38eb6548bb91c421261b4805dc44def9ca1a6eef6444ce35ad1669c0f1a3fc5d",
-                "sha256:3d7abcdd85aea3e6cdddb59af2350c7ab1ed764397f8eec97a038ad244d2d105",
-                "sha256:41a56b70e8139884eccb2f733c2f7378af06c82304959e174f8e7370af112e09",
-                "sha256:4a90725800caeaa160732d6b31f3f843ebd45d6b5f3eec9e8cc287e30f2805bf",
-                "sha256:6b82655dd8efeea69dbf85d00fca40013d7f503212bc5259056244961268b66e",
-                "sha256:6c6c9261d21e617c6dc5eacba35cb68ec36bb72adcff0dee63f8fbc899362588",
-                "sha256:77d339465dff3eb33c701430bcb9c325b60354698340229e1dff97745e6b3efa",
-                "sha256:791f409064d0a69dd20579345d852c59822c6aa087f23b07b1b4e28ff5880fcb",
-                "sha256:9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf",
-                "sha256:c1516db588987450b85595586605742879e50dcce923e8973f79529651545b57",
-                "sha256:c40571fe966393b212689aa17e32ed905924120737194b5d5c1b20b9ed0fb171",
-                "sha256:d412c1697c3853c6fc3cb9751b4915859c7afe6a277c2bf00acf287d56c4e625",
-                "sha256:d5154b1a25ec796b1aee12ac1b22f414f94752c5f94832f14d8d6c9ac40bcca6",
-                "sha256:d736b75c3f2cb96843a5c7f8d8ccc414768d34b0a75f466c05f3a739b406f10b",
-                "sha256:e8f6049c4878cb16960fbbfb22105e49d13d752d4d8371b55110941fb3b17800",
-                "sha256:f76aebc3358ade9eacf9bc2bb8ae589863a4f911611694103af05346637df1b7",
-                "sha256:fd67b306320dcadea700a8f79b9e671e607f8696e98ec255915c0c6d6b818503"
+                "sha256:0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2",
+                "sha256:1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55",
+                "sha256:1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf",
+                "sha256:2792d23d62ec51e50ce4d4b7d73de8f67a2fd3ea710dcbc8563a51a03fb07b01",
+                "sha256:3e0746410e73384e70d286f93abf2520035250aad8c5714240b0492a7302fdca",
+                "sha256:4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901",
+                "sha256:5883c06bb92f2e6c8181df7b39971a5fb436288db58b5a1c3967702d4278691d",
+                "sha256:5c97325a0ba6f9d041feb9390924614b60b99209a71a69c876f71052521d42a4",
+                "sha256:60e7f0f7f6d0eee8364b9a6304c2845b9c491ac706048c7e8cf47b83123b8dbf",
+                "sha256:76b4115d42a7dfc5d485d358728cdd8719be33cc5ec6ec08632a5d6fca2ed380",
+                "sha256:7dc869c0c75988e1c693d0e2d5b26034644399dd929bc049db55395b1379e044",
+                "sha256:834b386f2b8210dca38c71a6e0f4fd6922f7d3fcff935dbe3a570945acb1b545",
+                "sha256:8b77775f4b7df768967a7c8b3567e309f617dd5e99aeb886fa14dc1a0791141f",
+                "sha256:90319e4f002795ccfc9050110bbbaa16c944b1c37c0baeea43c5fb881693ae1f",
+                "sha256:b79e513d7aac42ae918db3ad1341a015488530d0bb2a6abcbdd10a3a829ccfd3",
+                "sha256:bb33d5a1cf360304754913a350edda36d5b8c5331a8237268c48f91253c3a364",
+                "sha256:bec1e7213c7cb00d67093247f8c4db156fd03075f49876957dca4711306d39c9",
+                "sha256:c5462d19336db4560041517dbb7759c21d181a67cb01b36ca109b2ae37d32418",
+                "sha256:c5652ea24d33585ea39eb6a6a15dac87a1206a692719ff45d53c5282e66d4a8f",
+                "sha256:d7806500e4f5bdd04095e849265e55de20d8cc4b661b038957354327f6d9b295",
+                "sha256:db3ccc4e37a6873045580d413fe79b68e47a681af8db2e046f1dacfa11f86eb3",
+                "sha256:dfe4a913e29b418d096e696ddd422d8a5d13ffba4ea91f9f60440a3b759b0187",
+                "sha256:eb942bfb6f84df5ce05dbf4b46673ffed0d3da59f13635ea9b926af3deb76926",
+                "sha256:f08f2e037bba04e707eebf4bc934f1972a315c883a9e0ebfa8a7756eabf9e357",
+                "sha256:fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==1.25.1"
+            "version": "==1.25.2"
         },
         "overrides": {
             "hashes": [
@@ -1125,11 +1109,19 @@
         },
         "pathspec": {
             "hashes": [
-                "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687",
-                "sha256:d8af70af76652554bd134c22b3e8a1cc46ed7d91edcdd721ef1a0c51a84a5293"
+                "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20",
+                "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==0.11.1"
+            "version": "==0.11.2"
+        },
+        "pexpect": {
+            "hashes": [
+                "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937",
+                "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"
+            ],
+            "markers": "sys_platform != 'win32'",
+            "version": "==4.8.0"
         },
         "pickleshare": {
             "hashes": [
@@ -1202,11 +1194,11 @@
         },
         "platformdirs": {
             "hashes": [
-                "sha256:1b42b450ad933e981d56e59f1b97495428c9bd60698baab9f3eb3d00d5822421",
-                "sha256:ad8291ae0ae5072f66c16945166cb11c63394c7a3ad1b1bc9828ca3162da8c2f"
+                "sha256:b45696dab2d7cc691a3226759c0d3b00c47c8b6e293d96f6436f733303f77f6d",
+                "sha256:d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==3.9.1"
+            "version": "==3.10.0"
         },
         "prometheus-client": {
             "hashes": [
@@ -1244,6 +1236,13 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==5.9.5"
         },
+        "ptyprocess": {
+            "hashes": [
+                "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35",
+                "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"
+            ],
+            "version": "==0.7.0"
+        },
         "pure-eval": {
             "hashes": [
                 "sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350",
@@ -1328,37 +1327,6 @@
             ],
             "version": "==2023.3"
         },
-        "pywin32": {
-            "hashes": [
-                "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d",
-                "sha256:1c73ea9a0d2283d889001998059f5eaaba3b6238f767c9cf2833b13e6a685f65",
-                "sha256:37257794c1ad39ee9be652da0462dc2e394c8159dfd913a8a4e8eb6fd346da0e",
-                "sha256:383229d515657f4e3ed1343da8be101000562bf514591ff383ae940cad65458b",
-                "sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4",
-                "sha256:5821ec52f6d321aa59e2db7e0a35b997de60c201943557d108af9d4ae1ec7040",
-                "sha256:70dba0c913d19f942a2db25217d9a1b726c278f483a919f1abfed79c9cf64d3a",
-                "sha256:72c5f621542d7bdd4fdb716227be0dd3f8565c11b280be6315b06ace35487d36",
-                "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8",
-                "sha256:a7639f51c184c0272e93f244eb24dafca9b1855707d94c192d4a0b4c01e1100e",
-                "sha256:e25fd5b485b55ac9c057f67d94bc203f3f6595078d1fb3b458c9c28b7153a802",
-                "sha256:e4c092e2589b5cf0d365849e73e02c391c1349958c5ac3e9d5ccb9a28e017b3a",
-                "sha256:e65028133d15b64d2ed8f06dd9fbc268352478d4f9289e69c190ecd6818b6407",
-                "sha256:e8ac1ae3601bee6ca9f7cb4b5363bf1c0badb935ef243c4733ff9a393b1690c0"
-            ],
-            "markers": "sys_platform == 'win32' and platform_python_implementation != 'PyPy'",
-            "version": "==306"
-        },
-        "pywinpty": {
-            "hashes": [
-                "sha256:0ffd287751ad871141dc9724de70ea21f7fc2ff1af50861e0d232cf70739d8c4",
-                "sha256:452f10ac9ff8ab9151aa8cea9e491a9612a12250b1899278c6a56bc184afb47f",
-                "sha256:6701867d42aec1239bc0fedf49a336570eb60eb886e81763db77ea2b6c533cc3",
-                "sha256:e244cffe29a894876e2cd251306efd0d8d64abd5ada0a46150a4a71c0b9ad5c5",
-                "sha256:e4e7f023c28ca7aa8e1313e53ba80a4d10171fe27857b7e02f99882dfe3e8638"
-            ],
-            "markers": "os_name == 'nt'",
-            "version": "==2.0.11"
-        },
         "pyyaml": {
             "hashes": [
                 "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc",
@@ -1836,14 +1804,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.2.1"
         },
-        "tomli": {
-            "hashes": [
-                "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc",
-                "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"
-            ],
-            "markers": "python_version < '3.11'",
-            "version": "==2.0.1"
-        },
         "tornado": {
             "hashes": [
                 "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4",
@@ -1869,14 +1829,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==5.9.0"
         },
-        "typing-extensions": {
-            "hashes": [
-                "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36",
-                "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"
-            ],
-            "markers": "python_version < '3.10'",
-            "version": "==4.7.1"
-        },
         "tzdata": {
             "hashes": [
                 "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a",
@@ -1936,14 +1888,6 @@
             ],
             "markers": "python_version >= '3.7'",
             "version": "==4.0.8"
-        },
-        "zipp": {
-            "hashes": [
-                "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0",
-                "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"
-            ],
-            "markers": "python_version < '3.10'",
-            "version": "==3.16.2"
         }
     },
     "develop": {}
diff --git a/script.py b/script.py
new file mode 100644
index 00000000..01212834
--- /dev/null
+++ b/script.py
@@ -0,0 +1,205 @@
+import requests
+import geopandas as gpd
+import pandas as pd
+import datetime
+from esridump.dumper import EsriDumper
+
+
+# Set the CRS to use for all layers
+use_crs = "EPSG:2272"
+
+
+class FeatureLayer:
+    '''
+    FeatureLayer is a class to represent a GIS dataset. It can be initialized with a URL to an Esri Feature Service, a SQL query to Carto, or a GeoDataFrame. 
+    '''
+
+    def __init__(self, name, esri_rest_urls=None, carto_sql_queries=None, gdf=None, crs=use_crs):
+        self.name = name
+        self.esri_rest_urls = [esri_rest_urls] if isinstance(
+            esri_rest_urls, str) else esri_rest_urls
+        self.carto_sql_queries = [carto_sql_queries] if isinstance(
+            carto_sql_queries, str) else carto_sql_queries
+        self.gdf = gdf
+        self.crs = crs
+
+        inputs = [self.esri_rest_urls, self.carto_sql_queries, self.gdf]
+        non_none_inputs = [i for i in inputs if i is not None]
+
+        if len(non_none_inputs) != 1:
+            raise ValueError(
+                'Exactly one of esri_rest_urls, carto_sql_queries, or gdf must be provided.')
+
+        if self.esri_rest_urls is not None:
+            self.type = 'esri'
+        elif self.carto_sql_queries is not None:
+            self.type = 'carto'
+        elif self.gdf is not None:
+            self.type = 'gdf'
+
+        self.load_data()
+
+    def load_data(self):
+        print(f'Loading data for {self.name}...')
+        if self.type == 'gdf':
+            self.gdf = gdf
+        else:
+            try:
+                if self.type == 'esri':
+                    if self.esri_rest_urls is None:
+                        raise ValueError(
+                            'Must provide a URL to load data from Esri')
+
+                    gdfs = []
+                    for url in self.esri_rest_urls:
+                        self.dumper = EsriDumper(url)
+                        features = [feature for feature in self.dumper]
+
+                        geojson_features = {
+                            "type": "FeatureCollection", "features": features}
+                        gdfs.append(gpd.GeoDataFrame.from_features(
+                            geojson_features, crs=self.crs))
+
+                    self.gdf = pd.concat(gdfs, ignore_index=True)
+
+                elif self.type == 'carto':
+                    if self.carto_sql_queries is None:
+                        raise ValueError(
+                            'Must provide a SQL query to load data from Carto')
+
+                    gdfs = []
+                    for sql_query in self.carto_sql_queries:
+                        response = requests.get(
+                            "https://phl.carto.com/api/v2/sql", params={"q": sql_query})
+
+                        data = response.json()["rows"]
+                        df = pd.DataFrame(data)
+                        gdf = gpd.GeoDataFrame(
+                            df, geometry=gpd.points_from_xy(df.x, df.y), crs=self.crs)
+
+                        gdfs.append(gdf)
+                    self.gdf = pd.concat(gdfs, ignore_index=True)
+
+            except Exception as e:
+                print(f'Error loading data for {self.name}: {e}')
+                self.gdf = None
+
+    def spatial_join(self, other_layer, how='left', predicate='intersects'):
+        '''
+        Spatial joins in this script are generally left intersect joins.
+        They also can create duplicates, so we drop duplicates after the join.
+        Note: We may want to revisit the duplicates.
+        '''
+        self.gdf = gpd.sjoin(self.gdf, other_layer.gdf,
+                             how=how, predicate=predicate)
+        self.gdf.drop(columns=['index_right'], inplace=True)
+        self.gdf.drop_duplicates(inplace=True)
+
+
+'''
+Load Vacant Properties Datasets
+'''
+
+vacant_props_layers_to_load = [
+    'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/', 'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/']
+
+
+vacant_properties = FeatureLayer(
+    name="Vacant Properties", esri_rest_urls=vacant_props_layers_to_load)
+
+
+# '''
+# Load City Owned Properties
+# '''
+city_owned_properties = FeatureLayer(
+    name='City Owned Properties', esri_rest_urls='https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/')
+
+# Discover which vacant properties are city owned using a spatial join
+vacant_properties.spatial_join(city_owned_properties)
+
+# There are some matches which aren't valid because of imprecise parcel boundaries. Use a match on OPA_ID and OPABRT to remove these.
+vacant_properties.gdf = vacant_properties.gdf.loc[~((vacant_properties.gdf['OPABRT'].notnull()) & (
+    vacant_properties.gdf['OPA_ID'] != vacant_properties.gdf['OPABRT']))]
+
+# Note: This removes some entries from the dataset, need to revisit this
+
+
+# '''
+# Load PHS Data
+# '''
+
+phs_layers_to_load = [
+    'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/', 'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/'
+]
+
+phs_properties = FeatureLayer(
+    name="PHS Properties", esri_rest_urls=phs_layers_to_load)
+
+phs_properties.gdf['COMM_PARTN'] = 'PHS'
+phs_properties.gdf = phs_properties.gdf[['COMM_PARTN', 'geometry']]
+
+vacant_properties.spatial_join(phs_properties)
+vacant_properties.gdf['COMM_PARTN'].fillna('None', inplace=True)
+
+
+# '''
+# L&I Data
+# '''
+
+# # L&I Complaints
+# one_year_ago = (datetime.datetime.now() -
+#                 datetime.timedelta(days=365)).strftime("%Y-%m-%d")
+
+# complaints_sql_query = f"SELECT address, service_request_id, subject, status, service_name, service_code, lat as y, lon as x FROM public_cases_fc WHERE requested_datetime >= '{one_year_ago}'"
+# l_and_i_complaints = FeatureLayer(
+#     name='L&I Complaints', carto_sql_queries=complaints_sql_query, type='carto')
+
+# # filter for only Status = 'Open'
+# l_and_i_complaints.gdf = l_and_i_complaints.gdf[l_and_i_complaints.gdf['status'] == 'Open']
+
+# # collapse complaints_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
+# l_and_i_complaints.gdf = l_and_i_complaints.gdf.groupby('address')['service_name'].apply(
+#     lambda x: '; '.join([val for val in x if val is not None])).reset_index()
+
+# # rename the column to 'li_complaints'
+# l_and_i_complaints.gdf.rename(
+#     columns={'service_name': 'li_complaints'}, inplace=True)
+
+# # L&I Code Violations
+# violations_sql_query = f"SELECT parcel_id_num, casenumber, casecreateddate, casetype, casestatus, violationnumber, violationcodetitle, violationstatus, opa_account_num, address, opa_owner, geocode_x as x, geocode_y as y FROM violations WHERE violationdate >= '{one_year_ago}'"
+# l_and_i_violations = FeatureLayer(
+#     name='L&I Code Violations', carto_sql_queries=violations_sql_query, type='carto')
+
+# # Manipulation of L&I data- not changed much from the original script
+# all_violations_count_df = l_and_i_violations.gdf.groupby(
+#     'opa_account_num').count().reset_index()[['opa_account_num', 'violationnumber']]
+# all_violations_count_df = all_violations_count_df.rename(
+#     columns={'violationnumber': 'all_violations_past_year'})
+# l_and_i_violations.gdf = l_and_i_violations.gdf[(
+#     l_and_i_violations.gdf['violationstatus'] == 'OPEN')]
+
+# open_violations_count_df = l_and_i_violations.gdf.groupby(
+#     'opa_account_num').count().reset_index()[['opa_account_num', 'violationnumber']]
+# open_violations_count_df = open_violations_count_df.rename(
+#     columns={'violationnumber': 'open_violations_past_year'})
+
+# # join the all_violations_count_df and open_violations_count_df dataframes on opa_account_num
+# violations_count_gdf = all_violations_count_df.merge(
+#     open_violations_count_df, how='left', on='opa_account_num')
+
+# # replace NaN values with 0
+# violations_count_gdf.fillna(0, inplace=True)
+
+# # convert the all_violations_past_year and open_violations_past_year columns to integers
+# violations_count_gdf['all_violations_past_year'] = violations_count_gdf['all_violations_past_year'].astype(
+#     int)
+# violations_count_gdf['open_violations_past_year'] = violations_count_gdf['open_violations_past_year'].astype(
+#     int)
+
+# # collapse violations_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
+# violations_gdf = l_and_i_violations.gdf.groupby('opa_account_num')['violationcodetitle'].apply(
+#     lambda x: '; '.join([val for val in x if val is not None])).reset_index()
+
+# # rename the column to 'li_violations'
+# violations_gdf.rename(
+#     columns={'violationcodetitle': 'li_code_violations'}, inplace=True)

From a81b7b20a9aa74bbabd6cb32abd9d3ebf644ee5b Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Wed, 2 Aug 2023 21:45:52 -0400
Subject: [PATCH 2/7] black formatting

---
 script.py | 110 ++++++++++++++++++++++++++++++++----------------------
 1 file changed, 66 insertions(+), 44 deletions(-)

diff --git a/script.py b/script.py
index 01212834..bdd90c51 100644
--- a/script.py
+++ b/script.py
@@ -10,16 +10,22 @@
 
 
 class FeatureLayer:
-    '''
-    FeatureLayer is a class to represent a GIS dataset. It can be initialized with a URL to an Esri Feature Service, a SQL query to Carto, or a GeoDataFrame. 
-    '''
+    """
+    FeatureLayer is a class to represent a GIS dataset. It can be initialized with a URL to an Esri Feature Service, a SQL query to Carto, or a GeoDataFrame.
+    """
 
-    def __init__(self, name, esri_rest_urls=None, carto_sql_queries=None, gdf=None, crs=use_crs):
+    def __init__(
+        self, name, esri_rest_urls=None, carto_sql_queries=None, gdf=None, crs=use_crs
+    ):
         self.name = name
-        self.esri_rest_urls = [esri_rest_urls] if isinstance(
-            esri_rest_urls, str) else esri_rest_urls
-        self.carto_sql_queries = [carto_sql_queries] if isinstance(
-            carto_sql_queries, str) else carto_sql_queries
+        self.esri_rest_urls = (
+            [esri_rest_urls] if isinstance(esri_rest_urls, str) else esri_rest_urls
+        )
+        self.carto_sql_queries = (
+            [carto_sql_queries]
+            if isinstance(carto_sql_queries, str)
+            else carto_sql_queries
+        )
         self.gdf = gdf
         self.crs = crs
 
@@ -28,27 +34,27 @@ def __init__(self, name, esri_rest_urls=None, carto_sql_queries=None, gdf=None,
 
         if len(non_none_inputs) != 1:
             raise ValueError(
-                'Exactly one of esri_rest_urls, carto_sql_queries, or gdf must be provided.')
+                "Exactly one of esri_rest_urls, carto_sql_queries, or gdf must be provided."
+            )
 
         if self.esri_rest_urls is not None:
-            self.type = 'esri'
+            self.type = "esri"
         elif self.carto_sql_queries is not None:
-            self.type = 'carto'
+            self.type = "carto"
         elif self.gdf is not None:
-            self.type = 'gdf'
+            self.type = "gdf"
 
         self.load_data()
 
     def load_data(self):
-        print(f'Loading data for {self.name}...')
-        if self.type == 'gdf':
+        print(f"Loading data for {self.name}...")
+        if self.type == "gdf":
             self.gdf = gdf
         else:
             try:
-                if self.type == 'esri':
+                if self.type == "esri":
                     if self.esri_rest_urls is None:
-                        raise ValueError(
-                            'Must provide a URL to load data from Esri')
+                        raise ValueError("Must provide a URL to load data from Esri")
 
                     gdfs = []
                     for url in self.esri_rest_urls:
@@ -56,70 +62,86 @@ def load_data(self):
                         features = [feature for feature in self.dumper]
 
                         geojson_features = {
-                            "type": "FeatureCollection", "features": features}
-                        gdfs.append(gpd.GeoDataFrame.from_features(
-                            geojson_features, crs=self.crs))
+                            "type": "FeatureCollection",
+                            "features": features,
+                        }
+                        gdfs.append(
+                            gpd.GeoDataFrame.from_features(
+                                geojson_features, crs=self.crs
+                            )
+                        )
 
                     self.gdf = pd.concat(gdfs, ignore_index=True)
 
-                elif self.type == 'carto':
+                elif self.type == "carto":
                     if self.carto_sql_queries is None:
                         raise ValueError(
-                            'Must provide a SQL query to load data from Carto')
+                            "Must provide a SQL query to load data from Carto"
+                        )
 
                     gdfs = []
                     for sql_query in self.carto_sql_queries:
                         response = requests.get(
-                            "https://phl.carto.com/api/v2/sql", params={"q": sql_query})
+                            "https://phl.carto.com/api/v2/sql", params={"q": sql_query}
+                        )
 
                         data = response.json()["rows"]
                         df = pd.DataFrame(data)
                         gdf = gpd.GeoDataFrame(
-                            df, geometry=gpd.points_from_xy(df.x, df.y), crs=self.crs)
+                            df, geometry=gpd.points_from_xy(df.x, df.y), crs=self.crs
+                        )
 
                         gdfs.append(gdf)
                     self.gdf = pd.concat(gdfs, ignore_index=True)
 
             except Exception as e:
-                print(f'Error loading data for {self.name}: {e}')
+                print(f"Error loading data for {self.name}: {e}")
                 self.gdf = None
 
-    def spatial_join(self, other_layer, how='left', predicate='intersects'):
-        '''
+    def spatial_join(self, other_layer, how="left", predicate="intersects"):
+        """
         Spatial joins in this script are generally left intersect joins.
         They also can create duplicates, so we drop duplicates after the join.
         Note: We may want to revisit the duplicates.
-        '''
-        self.gdf = gpd.sjoin(self.gdf, other_layer.gdf,
-                             how=how, predicate=predicate)
-        self.gdf.drop(columns=['index_right'], inplace=True)
+        """
+        self.gdf = gpd.sjoin(self.gdf, other_layer.gdf, how=how, predicate=predicate)
+        self.gdf.drop(columns=["index_right"], inplace=True)
         self.gdf.drop_duplicates(inplace=True)
 
 
-'''
+"""
 Load Vacant Properties Datasets
-'''
+"""
 
 vacant_props_layers_to_load = [
-    'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/', 'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/']
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/",
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/",
+]
 
 
 vacant_properties = FeatureLayer(
-    name="Vacant Properties", esri_rest_urls=vacant_props_layers_to_load)
+    name="Vacant Properties", esri_rest_urls=vacant_props_layers_to_load
+)
 
 
 # '''
 # Load City Owned Properties
 # '''
 city_owned_properties = FeatureLayer(
-    name='City Owned Properties', esri_rest_urls='https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/')
+    name="City Owned Properties",
+    esri_rest_urls="https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/",
+)
 
 # Discover which vacant properties are city owned using a spatial join
 vacant_properties.spatial_join(city_owned_properties)
 
 # There are some matches which aren't valid because of imprecise parcel boundaries. Use a match on OPA_ID and OPABRT to remove these.
-vacant_properties.gdf = vacant_properties.gdf.loc[~((vacant_properties.gdf['OPABRT'].notnull()) & (
-    vacant_properties.gdf['OPA_ID'] != vacant_properties.gdf['OPABRT']))]
+vacant_properties.gdf = vacant_properties.gdf.loc[
+    ~(
+        (vacant_properties.gdf["OPABRT"].notnull())
+        & (vacant_properties.gdf["OPA_ID"] != vacant_properties.gdf["OPABRT"])
+    )
+]
 
 # Note: This removes some entries from the dataset, need to revisit this
 
@@ -129,17 +151,17 @@ def spatial_join(self, other_layer, how='left', predicate='intersects'):
 # '''
 
 phs_layers_to_load = [
-    'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/', 'https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/'
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/",
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/",
 ]
 
-phs_properties = FeatureLayer(
-    name="PHS Properties", esri_rest_urls=phs_layers_to_load)
+phs_properties = FeatureLayer(name="PHS Properties", esri_rest_urls=phs_layers_to_load)
 
-phs_properties.gdf['COMM_PARTN'] = 'PHS'
-phs_properties.gdf = phs_properties.gdf[['COMM_PARTN', 'geometry']]
+phs_properties.gdf["COMM_PARTN"] = "PHS"
+phs_properties.gdf = phs_properties.gdf[["COMM_PARTN", "geometry"]]
 
 vacant_properties.spatial_join(phs_properties)
-vacant_properties.gdf['COMM_PARTN'].fillna('None', inplace=True)
+vacant_properties.gdf["COMM_PARTN"].fillna("None", inplace=True)
 
 
 # '''

From 521f9c4efd41f09be732767c9635baecf6139620 Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Wed, 2 Aug 2023 21:52:18 -0400
Subject: [PATCH 3/7] clean up

---
 script.py | 75 +++++--------------------------------------------------
 1 file changed, 6 insertions(+), 69 deletions(-)

diff --git a/script.py b/script.py
index bdd90c51..18fc9c53 100644
--- a/script.py
+++ b/script.py
@@ -124,9 +124,9 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
 )
 
 
-# '''
-# Load City Owned Properties
-# '''
+"""
+Load City Owned Properties
+"""
 city_owned_properties = FeatureLayer(
     name="City Owned Properties",
     esri_rest_urls="https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/",
@@ -146,9 +146,9 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
 # Note: This removes some entries from the dataset, need to revisit this
 
 
-# '''
-# Load PHS Data
-# '''
+"""
+Load PHS Data
+"""
 
 phs_layers_to_load = [
     "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/",
@@ -162,66 +162,3 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
 
 vacant_properties.spatial_join(phs_properties)
 vacant_properties.gdf["COMM_PARTN"].fillna("None", inplace=True)
-
-
-# '''
-# L&I Data
-# '''
-
-# # L&I Complaints
-# one_year_ago = (datetime.datetime.now() -
-#                 datetime.timedelta(days=365)).strftime("%Y-%m-%d")
-
-# complaints_sql_query = f"SELECT address, service_request_id, subject, status, service_name, service_code, lat as y, lon as x FROM public_cases_fc WHERE requested_datetime >= '{one_year_ago}'"
-# l_and_i_complaints = FeatureLayer(
-#     name='L&I Complaints', carto_sql_queries=complaints_sql_query, type='carto')
-
-# # filter for only Status = 'Open'
-# l_and_i_complaints.gdf = l_and_i_complaints.gdf[l_and_i_complaints.gdf['status'] == 'Open']
-
-# # collapse complaints_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
-# l_and_i_complaints.gdf = l_and_i_complaints.gdf.groupby('address')['service_name'].apply(
-#     lambda x: '; '.join([val for val in x if val is not None])).reset_index()
-
-# # rename the column to 'li_complaints'
-# l_and_i_complaints.gdf.rename(
-#     columns={'service_name': 'li_complaints'}, inplace=True)
-
-# # L&I Code Violations
-# violations_sql_query = f"SELECT parcel_id_num, casenumber, casecreateddate, casetype, casestatus, violationnumber, violationcodetitle, violationstatus, opa_account_num, address, opa_owner, geocode_x as x, geocode_y as y FROM violations WHERE violationdate >= '{one_year_ago}'"
-# l_and_i_violations = FeatureLayer(
-#     name='L&I Code Violations', carto_sql_queries=violations_sql_query, type='carto')
-
-# # Manipulation of L&I data- not changed much from the original script
-# all_violations_count_df = l_and_i_violations.gdf.groupby(
-#     'opa_account_num').count().reset_index()[['opa_account_num', 'violationnumber']]
-# all_violations_count_df = all_violations_count_df.rename(
-#     columns={'violationnumber': 'all_violations_past_year'})
-# l_and_i_violations.gdf = l_and_i_violations.gdf[(
-#     l_and_i_violations.gdf['violationstatus'] == 'OPEN')]
-
-# open_violations_count_df = l_and_i_violations.gdf.groupby(
-#     'opa_account_num').count().reset_index()[['opa_account_num', 'violationnumber']]
-# open_violations_count_df = open_violations_count_df.rename(
-#     columns={'violationnumber': 'open_violations_past_year'})
-
-# # join the all_violations_count_df and open_violations_count_df dataframes on opa_account_num
-# violations_count_gdf = all_violations_count_df.merge(
-#     open_violations_count_df, how='left', on='opa_account_num')
-
-# # replace NaN values with 0
-# violations_count_gdf.fillna(0, inplace=True)
-
-# # convert the all_violations_past_year and open_violations_past_year columns to integers
-# violations_count_gdf['all_violations_past_year'] = violations_count_gdf['all_violations_past_year'].astype(
-#     int)
-# violations_count_gdf['open_violations_past_year'] = violations_count_gdf['open_violations_past_year'].astype(
-#     int)
-
-# # collapse violations_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
-# violations_gdf = l_and_i_violations.gdf.groupby('opa_account_num')['violationcodetitle'].apply(
-#     lambda x: '; '.join([val for val in x if val is not None])).reset_index()
-
-# # rename the column to 'li_violations'
-# violations_gdf.rename(
-#     columns={'violationcodetitle': 'li_code_violations'}, inplace=True)

From 16fc1d0608a64ce7f4f53423b8d8be604eb55346 Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Thu, 24 Aug 2023 22:02:10 -0400
Subject: [PATCH 4/7] starting to work

---
 Pipfile                                       |   3 +
 Pipfile.lock                                  | 441 ++++++++++++------
 README.md                                     |  26 +-
 .../2023_02_02_vacant_lots_layer.ipynb        |   0
 constants.py => archived/constants.py         |   0
 data_utils.py => archived/data_utils.py       |   0
 process_data.py => archived/process_data.py   |   0
 notes.md                                      |   1 +
 src/__init__.py                               |   0
 src/classes/__init__.py                       |   0
 script.py => src/classes/featurelayer.py      | 130 +++---
 src/config/__init__.py                        |   0
 src/config/config.py                          |   2 +
 src/config/psql.py                            |   6 +
 src/constants/__init__.py                     |   0
 src/constants/services.py                     |  24 +
 src/data_utils/__init__.py                    |   0
 src/data_utils/city_owned_properties.py       |  21 +
 src/data_utils/l_and_i.py                     |  98 ++++
 src/data_utils/phs_properties.py              |  16 +
 src/script.py                                 |  42 ++
 21 files changed, 589 insertions(+), 221 deletions(-)
 rename 2023_02_02_vacant_lots_layer.ipynb => archived/2023_02_02_vacant_lots_layer.ipynb (100%)
 rename constants.py => archived/constants.py (100%)
 rename data_utils.py => archived/data_utils.py (100%)
 rename process_data.py => archived/process_data.py (100%)
 create mode 100644 notes.md
 create mode 100644 src/__init__.py
 create mode 100644 src/classes/__init__.py
 rename script.py => src/classes/featurelayer.py (60%)
 create mode 100644 src/config/__init__.py
 create mode 100644 src/config/config.py
 create mode 100644 src/config/psql.py
 create mode 100644 src/constants/__init__.py
 create mode 100644 src/constants/services.py
 create mode 100644 src/data_utils/__init__.py
 create mode 100644 src/data_utils/city_owned_properties.py
 create mode 100644 src/data_utils/l_and_i.py
 create mode 100644 src/data_utils/phs_properties.py
 create mode 100644 src/script.py

diff --git a/Pipfile b/Pipfile
index 7dfbb58d..ba475f75 100644
--- a/Pipfile
+++ b/Pipfile
@@ -16,6 +16,9 @@ mapclassify = "*"
 black = "*"
 fiona = "*"
 esridump = "*"
+sqlalchemy = "*"
+psycopg2-binary = "*"
+geoalchemy2 = "*"
 
 [dev-packages]
 
diff --git a/Pipfile.lock b/Pipfile.lock
index 7883cc7a..f614ef52 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "b1e540e2ca2141f8538cc433316660fac0aa0a767b4327edec27fbe92da97600"
+            "sha256": "304d3732a0e4467e0533740d3ca4dcd9be9a293b58ad52e9f48d6559f0013bfe"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -43,11 +43,11 @@
         },
         "argon2-cffi": {
             "hashes": [
-                "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80",
-                "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"
+                "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08",
+                "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"
             ],
-            "markers": "python_version >= '3.6'",
-            "version": "==21.3.0"
+            "markers": "python_version >= '3.7'",
+            "version": "==23.1.0"
         },
         "argon2-cffi-bindings": {
             "hashes": [
@@ -410,27 +410,27 @@
         },
         "debugpy": {
             "hashes": [
-                "sha256:05d1b288167ce3bfc8e1912ebed036207a27b9569ae4476f18287902501689c6",
-                "sha256:1ca76d3ebb0e6368e107cf2e005e848d3c7705a5b513fdf65470a6f4e49a2de7",
-                "sha256:1fe3baa28f5a14d8d2a60dded9ea088e27b33f1854ae9a0a1faa1ba03a8b7e47",
-                "sha256:2345beced3e79fd8ac4158e839a1604d5cccd19beb45561a1ffe2e5b33465f28",
-                "sha256:3b7091d908dec70022b8966c32b1e9eaf183ff05291edf1d147fee153f4cb9f8",
-                "sha256:406b3a6cb7548d73260f69a511178ec9196779cafda68e563488c6f94cc88670",
-                "sha256:4172383b961a2334d29168c7f7b24f2f99d29291a945016986c78a5683fba915",
-                "sha256:5502e14de6b7241ecf7c4fa4ec6dd61d0824da7a09020c7ffe7be4cd09d36f24",
-                "sha256:591aac0e69bc75102d9f9294f1228e5d9ff9aa17b8c88e48b1bbb3dab8a54dcc",
-                "sha256:6830947f68b41cd6abe20941ec3303a8452c40ff5fe3637c6efe233e395ecebc",
-                "sha256:6ca1c92e30e2aaeca156d5bd76e1587c23e332474a7b12e1900dd632b31ce05e",
-                "sha256:8c1f5a3286fb633f691c594649e9d2e8e30292c9eaf49e38d7da525151b33a83",
-                "sha256:959f9b8181a4c544b067daff8d881cd3ac4c7aec1a3a4f41f81c529795b3d864",
-                "sha256:95f7ce92450b72abcf0c479539a7d00c20e68f1f1fb447eef0b08d2a635d96d7",
-                "sha256:bb27b8e08f8e60705de6cf05b5da4c21e5a0bc2ca73f06fc36646f456df18ff5",
-                "sha256:f16bb157b6018ce6a23b64653a6b1892f046cc2b0576df1794c6b22f9fd82118",
-                "sha256:f4a7193cec3f1e188963f6e8699e1187f758a0a4bbce511b3ad40caf618fc888",
-                "sha256:f7a80c50b89d8fb49c9e5b6ee28c0bfb822fbd33fef0f2f9843724d0d1984e4e"
+                "sha256:038c51268367c9c935905a90b1c2d2dbfe304037c27ba9d19fe7409f8cdc710c",
+                "sha256:1093a5c541af079c13ac8c70ab8b24d1d35c8cacb676306cf11e57f699c02926",
+                "sha256:3370ef1b9951d15799ef7af41f8174194f3482ee689988379763ef61a5456426",
+                "sha256:38651c3639a4e8bbf0ca7e52d799f6abd07d622a193c406be375da4d510d968d",
+                "sha256:3de5d0f97c425dc49bce4293df6a04494309eedadd2b52c22e58d95107e178d9",
+                "sha256:4b9eba71c290852f959d2cf8a03af28afd3ca639ad374d393d53d367f7f685b2",
+                "sha256:65b28435a17cba4c09e739621173ff90c515f7b9e8ea469b92e3c28ef8e5cdfb",
+                "sha256:72f5d2ecead8125cf669e62784ef1e6300f4067b0f14d9f95ee00ae06fc7c4f7",
+                "sha256:85969d864c45f70c3996067cfa76a319bae749b04171f2cdeceebe4add316155",
+                "sha256:890f7ab9a683886a0f185786ffbda3b46495c4b929dab083b8c79d6825832a52",
+                "sha256:903bd61d5eb433b6c25b48eae5e23821d4c1a19e25c9610205f5aeaccae64e32",
+                "sha256:92b6dae8bfbd497c90596bbb69089acf7954164aea3228a99d7e43e5267f5b36",
+                "sha256:973a97ed3b434eab0f792719a484566c35328196540676685c975651266fccf9",
+                "sha256:d16882030860081e7dd5aa619f30dec3c2f9a421e69861125f83cc372c94e57d",
+                "sha256:d4ac7a4dba28801d184b7fc0e024da2635ca87d8b0a825c6087bb5168e3c0d28",
+                "sha256:eea8d8cfb9965ac41b99a61f8e755a8f50e9a20330938ad8271530210f54e09c",
+                "sha256:f0851403030f3975d6e2eaa4abf73232ab90b98f041e3c09ba33be2beda43fcf",
+                "sha256:fe87ec0182ef624855d05e6ed7e0b7cb1359d2ffa2a925f8ec2d22e98b75d0ca"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.6.8"
+            "version": "==1.6.7.post1"
         },
         "decorator": {
             "hashes": [
@@ -543,6 +543,14 @@
             ],
             "version": "==1.5.1"
         },
+        "geoalchemy2": {
+            "hashes": [
+                "sha256:0830c98f83d6b1706e62b5544793d304e2853493d6e70ac18444c13748c3d1c7",
+                "sha256:620b31cbf97a368b2486dbcfcd36da2081827e933d4163bcb942043b79b545e8"
+            ],
+            "index": "pypi",
+            "version": "==0.14.1"
+        },
         "geopandas": {
             "hashes": [
                 "sha256:101cfd0de54bcf9e287a55b5ea17ebe0db53a5e25a28bacf100143d0507cabd9",
@@ -561,11 +569,11 @@
         },
         "ipykernel": {
             "hashes": [
-                "sha256:e342ce84712861be4b248c4a73472be4702c1b0dd77448bfd6bcfb3af9d5ddf9",
-                "sha256:f0042e867ac3f6bca1679e6a88cbd6a58ed93a44f9d0866aecde6efe8de76659"
+                "sha256:050391364c0977e768e354bdb60cbbfbee7cbb943b1af1618382021136ffd42f",
+                "sha256:c8a2430b357073b37c76c21c52184db42f6b4b0e438e1eb7df3c4440d120497c"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==6.25.0"
+            "version": "==6.25.1"
         },
         "ipython": {
             "hashes": [
@@ -615,11 +623,11 @@
         },
         "joblib": {
             "hashes": [
-                "sha256:1f937906df65329ba98013dc9692fe22a4c5e4a648112de500508b18a21b41e3",
-                "sha256:89cf0529520e01b3de7ac7b74a8102c90d16d54c64b5dd98cafcd14307fdf915"
+                "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1",
+                "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.3.1"
+            "version": "==1.3.2"
         },
         "json5": {
             "hashes": [
@@ -637,11 +645,11 @@
         },
         "jsonschema": {
             "hashes": [
-                "sha256:ce71d2f8c7983ef75a756e568317bf54bc531dc3ad7e66a128eae0d51623d8a3",
-                "sha256:dc274409c36175aad949c68e5ead0853aaffbe8e88c830ae66bb3c7a1728ad2d"
+                "sha256:043dc26a3845ff09d20e4420d6012a9c91c9aa8999fa184e7efcfeccb41e32cb",
+                "sha256:6e1e7569ac13be8139b2dd2c21a55d350066ee3f80df06c608b398cdc6f30e8f"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.18.6"
+            "version": "==4.19.0"
         },
         "jsonschema-specifications": {
             "hashes": [
@@ -702,11 +710,11 @@
         },
         "jupyter-server": {
             "hashes": [
-                "sha256:36da0a266d31a41ac335a366c88933c17dfa5bb817a48f5c02c16d303bc9477f",
-                "sha256:6a77912aff643e53fa14bdb2634884b52b784a4be77ce8e93f7283faed0f0849"
+                "sha256:59838bf20759354e2b4222fed702948a934cb21a503e21cd5b03706a456391d9",
+                "sha256:76b4ae0b568c331acc9aa904fc4a1194ef9bdefa69f232deb3f3cae802528c05"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.7.0"
+            "version": "==2.7.1"
         },
         "jupyter-server-terminals": {
             "hashes": [
@@ -718,11 +726,11 @@
         },
         "jupyterlab": {
             "hashes": [
-                "sha256:d369944391b1d15f2d1f3cb965fb67352956279b2ae6f03ce7947a43940a8301",
-                "sha256:e14d1ce46a613028111d0d476a1d7d6b094003b7462bac669f5b478317abcb39"
+                "sha256:13b3a326e7b95d72746fe20dbe80ee1e71165d6905e01ceaf1320eb809cb1b47",
+                "sha256:de49deb75f9b9aec478ed04754cbefe9c5d22fd796a5783cdc65e212983d3611"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.0.3"
+            "version": "==4.0.5"
         },
         "jupyterlab-pygments": {
             "hashes": [
@@ -967,11 +975,11 @@
         },
         "nbconvert": {
             "hashes": [
-                "sha256:3022adadff3f86578a47fab7c2228bb3ca9c56a24345642a22f917f6168b48fc",
-                "sha256:4a5996bf5f3cd16aa0431897ba1aa4c64842c2079f434b3dc6b8c4b252ef3355"
+                "sha256:1113d039fa3fc3a846ffa5a3b0a019e85aaa94c566a09fa0c400fb7638e46087",
+                "sha256:ace26f4386d08eb5c55833596a942048c5502a95e05590cb523826a749a40a37"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==7.7.3"
+            "version": "==7.7.4"
         },
         "nbformat": {
             "hashes": [
@@ -999,11 +1007,11 @@
         },
         "notebook": {
             "hashes": [
-                "sha256:2e16ad4e63ea89f7efbe212ee7c1693fcfa5ab55ffef75047530f74af4bd926c",
-                "sha256:35327476042140e8739ff8fcfecdc915658ae72b4db72d6e3b537badcdbf9e35"
+                "sha256:c77b1499dc9b07ce4f4f26990dcb25b2107b434f2536766b51a72a4228d9a4b6",
+                "sha256:d70d6a07418c829bd5f54337ce993b7105261d9026f9d3fe68e9b8aa1a20da9a"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==7.0.1"
+            "version": "==7.0.2"
         },
         "notebook-shim": {
             "hashes": [
@@ -1046,11 +1054,11 @@
         },
         "overrides": {
             "hashes": [
-                "sha256:6187d8710a935d09b0bcef8238301d6ee2569d2ac1ae0ec39a8c7924e27f58ca",
-                "sha256:8b97c6c1e1681b78cbc9424b138d880f0803c2254c5ebaabdde57bb6c62093f2"
+                "sha256:3ad24583f86d6d7a49049695efe9933e67ba62f0c7625d53c59fa832ce4b8b7d",
+                "sha256:9502a3cca51f4fac40b5feca985b6703a5c1f6ad815588a7ca9e285b9dca6757"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==7.3.1"
+            "version": "==7.4.0"
         },
         "packaging": {
             "hashes": [
@@ -1236,6 +1244,72 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==5.9.5"
         },
+        "psycopg2-binary": {
+            "hashes": [
+                "sha256:00d8db270afb76f48a499f7bb8fa70297e66da67288471ca873db88382850bf4",
+                "sha256:024eaeb2a08c9a65cd5f94b31ace1ee3bb3f978cd4d079406aef85169ba01f08",
+                "sha256:094af2e77a1976efd4956a031028774b827029729725e136514aae3cdf49b87b",
+                "sha256:1011eeb0c51e5b9ea1016f0f45fa23aca63966a4c0afcf0340ccabe85a9f65bd",
+                "sha256:11abdbfc6f7f7dea4a524b5f4117369b0d757725798f1593796be6ece20266cb",
+                "sha256:122641b7fab18ef76b18860dd0c772290566b6fb30cc08e923ad73d17461dc63",
+                "sha256:17cc17a70dfb295a240db7f65b6d8153c3d81efb145d76da1e4a096e9c5c0e63",
+                "sha256:18f12632ab516c47c1ac4841a78fddea6508a8284c7cf0f292cb1a523f2e2379",
+                "sha256:1b918f64a51ffe19cd2e230b3240ba481330ce1d4b7875ae67305bd1d37b041c",
+                "sha256:1c31c2606ac500dbd26381145684d87730a2fac9a62ebcfbaa2b119f8d6c19f4",
+                "sha256:26484e913d472ecb6b45937ea55ce29c57c662066d222fb0fbdc1fab457f18c5",
+                "sha256:2993ccb2b7e80844d534e55e0f12534c2871952f78e0da33c35e648bf002bbff",
+                "sha256:2b04da24cbde33292ad34a40db9832a80ad12de26486ffeda883413c9e1b1d5e",
+                "sha256:2dec5a75a3a5d42b120e88e6ed3e3b37b46459202bb8e36cd67591b6e5feebc1",
+                "sha256:2df562bb2e4e00ee064779902d721223cfa9f8f58e7e52318c97d139cf7f012d",
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+                "sha256:4ecc15666f16f97709106d87284c136cdc82647e1c3f8392a672616aed3c7151",
+                "sha256:4eec5d36dbcfc076caab61a2114c12094c0b7027d57e9e4387b634e8ab36fd44",
+                "sha256:4fe13712357d802080cfccbf8c6266a3121dc0e27e2144819029095ccf708372",
+                "sha256:51d1b42d44f4ffb93188f9b39e6d1c82aa758fdb8d9de65e1ddfe7a7d250d7ad",
+                "sha256:59f7e9109a59dfa31efa022e94a244736ae401526682de504e87bd11ce870c22",
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+                "sha256:642df77484b2dcaf87d4237792246d8068653f9e0f5c025e2c692fc56b0dda70",
+                "sha256:6822c9c63308d650db201ba22fe6648bd6786ca6d14fdaf273b17e15608d0852",
+                "sha256:692df8763b71d42eb8343f54091368f6f6c9cfc56dc391858cdb3c3ef1e3e584",
+                "sha256:6d92e139ca388ccfe8c04aacc163756e55ba4c623c6ba13d5d1595ed97523e4b",
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+                "sha256:7db7b9b701974c96a88997d458b38ccb110eba8f805d4b4f74944aac48639b42",
+                "sha256:81d5dd2dd9ab78d31a451e357315f201d976c131ca7d43870a0e8063b6b7a1ec",
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+                "sha256:ea5f8ee87f1eddc818fc04649d952c526db4426d26bab16efbe5a0c52b27d6ab",
+                "sha256:eb1c0e682138f9067a58fc3c9a9bf1c83d8e08cfbee380d858e63196466d5c86",
+                "sha256:eb3b8d55924a6058a26db69fb1d3e7e32695ff8b491835ba9f479537e14dcf9f",
+                "sha256:ee919b676da28f78f91b464fb3e12238bd7474483352a59c8a16c39dfc59f0c5",
+                "sha256:f02f4a72cc3ab2565c6d9720f0343cb840fb2dc01a2e9ecb8bc58ccf95dc5c06",
+                "sha256:f4f37bbc6588d402980ffbd1f3338c871368fb4b1cfa091debe13c68bb3852b3",
+                "sha256:f8651cf1f144f9ee0fa7d1a1df61a9184ab72962531ca99f077bbdcba3947c58",
+                "sha256:f955aa50d7d5220fcb6e38f69ea126eafecd812d96aeed5d5f3597f33fad43bb",
+                "sha256:fc10da7e7df3380426521e8c1ed975d22df678639da2ed0ec3244c3dc2ab54c8",
+                "sha256:fdca0511458d26cf39b827a663d7d87db6f32b93efc22442a742035728603d5f"
+            ],
+            "index": "pypi",
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+                "sha256:e61f091c3ba0c3578411ef505992d356a812fb200643eab27f4f70eed34a29ef",
+                "sha256:e8a701123029cc240cea61dd2d16ad57cab4691804143ce80ecd9286b464d180",
+                "sha256:eadbefd5e92ef8a345f0525b5cfd01cf4e4cc651a2cffb8f23c0dd184975d787",
+                "sha256:f32260e556a983bc5c7ed588d04c942c9a8f9c2e99213fec11a031e316874c7e",
+                "sha256:f8115e303280ba09f3898194791a153862cbf9eef722ad8f7f741987ee2a97c7",
+                "sha256:fedbdc753827cf014c01dbbee9c3be17e5a208dcd1bf8641ce2cd29580d1f0d4"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==25.1.0"
+            "version": "==25.1.1"
         },
         "qtconsole": {
             "hashes": [
@@ -1497,11 +1587,11 @@
         },
         "referencing": {
             "hashes": [
-                "sha256:47237742e990457f7512c7d27486394a9aadaf876cbfaa4be65b27b4f4d47c6b",
-                "sha256:c257b08a399b6c2f5a3510a50d28ab5dbc7bbde049bcaf954d43c446f83ab548"
+                "sha256:449b6669b6121a9e96a7f9e410b245d471e8d48964c67113ce9afe50c8dd7bdf",
+                "sha256:794ad8003c65938edcdbc027f1933215e0d0ccc0291e3ce20a4d87432b59efc0"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.30.0"
+            "version": "==0.30.2"
         },
         "requests": {
             "hashes": [
@@ -1692,11 +1782,11 @@
         },
         "setuptools": {
             "hashes": [
-                "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f",
-                "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"
+                "sha256:d59c97e7b774979a5ccb96388efc9eb65518004537e85d52e81eaee89ab6dd91",
+                "sha256:e13e1b0bc760e9b0127eda042845999b2f913e12437046e663b833aa96d89715"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==68.0.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==68.1.0"
         },
         "shapely": {
             "hashes": [
@@ -1773,6 +1863,53 @@
             "markers": "python_version >= '3.7'",
             "version": "==2.4.1"
         },
+        "sqlalchemy": {
+            "hashes": [
+                "sha256:1506e988ebeaaf316f183da601f24eedd7452e163010ea63dbe52dc91c7fc70e",
+                "sha256:1a58052b5a93425f656675673ef1f7e005a3b72e3f2c91b8acca1b27ccadf5f4",
+                "sha256:1b74eeafaa11372627ce94e4dc88a6751b2b4d263015b3523e2b1e57291102f0",
+                "sha256:1be86ccea0c965a1e8cd6ccf6884b924c319fcc85765f16c69f1ae7148eba64b",
+                "sha256:1d35d49a972649b5080557c603110620a86aa11db350d7a7cb0f0a3f611948a0",
+                "sha256:243d0fb261f80a26774829bc2cee71df3222587ac789b7eaf6555c5b15651eed",
+                "sha256:26a3399eaf65e9ab2690c07bd5cf898b639e76903e0abad096cd609233ce5208",
+                "sha256:27d554ef5d12501898d88d255c54eef8414576f34672e02fe96d75908993cf53",
+                "sha256:3364b7066b3c7f4437dd345d47271f1251e0cfb0aba67e785343cdbdb0fff08c",
+                "sha256:3423dc2a3b94125094897118b52bdf4d37daf142cbcf26d48af284b763ab90e9",
+                "sha256:3c6aceebbc47db04f2d779db03afeaa2c73ea3f8dcd3987eb9efdb987ffa09a3",
+                "sha256:3ce5e81b800a8afc870bb8e0a275d81957e16f8c4b62415a7b386f29a0cb9763",
+                "sha256:411e7f140200c02c4b953b3dbd08351c9f9818d2bd591b56d0fa0716bd014f1e",
+                "sha256:4cde2e1096cbb3e62002efdb7050113aa5f01718035ba9f29f9d89c3758e7e4e",
+                "sha256:5768c268df78bacbde166b48be788b83dddaa2a5974b8810af422ddfe68a9bc8",
+                "sha256:599ccd23a7146e126be1c7632d1d47847fa9f333104d03325c4e15440fc7d927",
+                "sha256:5ed61e3463021763b853628aef8bc5d469fe12d95f82c74ef605049d810f3267",
+                "sha256:63a368231c53c93e2b67d0c5556a9836fdcd383f7e3026a39602aad775b14acf",
+                "sha256:63e73da7fb030ae0a46a9ffbeef7e892f5def4baf8064786d040d45c1d6d1dc5",
+                "sha256:6eb6d77c31e1bf4268b4d61b549c341cbff9842f8e115ba6904249c20cb78a61",
+                "sha256:6f8a934f9dfdf762c844e5164046a9cea25fabbc9ec865c023fe7f300f11ca4a",
+                "sha256:6fe7d61dc71119e21ddb0094ee994418c12f68c61b3d263ebaae50ea8399c4d4",
+                "sha256:759b51346aa388c2e606ee206c0bc6f15a5299f6174d1e10cadbe4530d3c7a98",
+                "sha256:76fdfc0f6f5341987474ff48e7a66c3cd2b8a71ddda01fa82fedb180b961630a",
+                "sha256:77d37c1b4e64c926fa3de23e8244b964aab92963d0f74d98cbc0783a9e04f501",
+                "sha256:79543f945be7a5ada9943d555cf9b1531cfea49241809dd1183701f94a748624",
+                "sha256:79fde625a0a55220d3624e64101ed68a059c1c1f126c74f08a42097a72ff66a9",
+                "sha256:7d3f175410a6db0ad96b10bfbb0a5530ecd4fcf1e2b5d83d968dd64791f810ed",
+                "sha256:8dd77fd6648b677d7742d2c3cc105a66e2681cc5e5fb247b88c7a7b78351cf74",
+                "sha256:a3f0dd6d15b6dc8b28a838a5c48ced7455c3e1fb47b89da9c79cc2090b072a50",
+                "sha256:bcb04441f370cbe6e37c2b8d79e4af9e4789f626c595899d94abebe8b38f9a4d",
+                "sha256:c3d99ba99007dab8233f635c32b5cd24fb1df8d64e17bc7df136cedbea427897",
+                "sha256:ca8a5ff2aa7f3ade6c498aaafce25b1eaeabe4e42b73e25519183e4566a16fc6",
+                "sha256:cb0d3e94c2a84215532d9bcf10229476ffd3b08f481c53754113b794afb62d14",
+                "sha256:d1b09ba72e4e6d341bb5bdd3564f1cea6095d4c3632e45dc69375a1dbe4e26ec",
+                "sha256:d32b5ffef6c5bcb452723a496bad2d4c52b346240c59b3e6dba279f6dcc06c14",
+                "sha256:d3793dcf5bc4d74ae1e9db15121250c2da476e1af8e45a1d9a52b1513a393459",
+                "sha256:dd81466bdbc82b060c3c110b2937ab65ace41dfa7b18681fdfad2f37f27acdd7",
+                "sha256:e4e571af672e1bb710b3cc1a9794b55bce1eae5aed41a608c0401885e3491179",
+                "sha256:ea8186be85da6587456c9ddc7bf480ebad1a0e6dcbad3967c4821233a4d4df57",
+                "sha256:eefebcc5c555803065128401a1e224a64607259b5eb907021bf9b175f315d2a6"
+            ],
+            "index": "pypi",
+            "version": "==2.0.20"
+        },
         "stack-data": {
             "hashes": [
                 "sha256:32d2dd0376772d01b6cb9fc996f3c8b57a357089dec328ed4b6553d037eaf815",
@@ -1806,20 +1943,20 @@
         },
         "tornado": {
             "hashes": [
-                "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4",
-                "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf",
-                "sha256:29e71c847a35f6e10ca3b5c2990a52ce38b233019d8e858b755ea6ce4dcdd19d",
-                "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba",
-                "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe",
-                "sha256:6a0848f1aea0d196a7c4f6772197cbe2abc4266f836b0aac76947872cd29b411",
-                "sha256:7efcbcc30b7c654eb6a8c9c9da787a851c18f8ccd4a5a3a95b05c7accfa068d2",
-                "sha256:834ae7540ad3a83199a8da8f9f2d383e3c3d5130a328889e4cc991acc81e87a0",
-                "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c",
-                "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f",
-                "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829"
+                "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f",
+                "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5",
+                "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d",
+                "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3",
+                "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2",
+                "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a",
+                "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16",
+                "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a",
+                "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17",
+                "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0",
+                "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==6.3.2"
+            "version": "==6.3.3"
         },
         "traitlets": {
             "hashes": [
@@ -1829,6 +1966,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==5.9.0"
         },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36",
+                "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.7.1"
+        },
         "tzdata": {
             "hashes": [
                 "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a",
diff --git a/README.md b/README.md
index bb2bf54f..4aa9406b 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,36 @@
 # vacant-lots-proj
+
 Gun crime x vacant lots mapping tool layer
 
 # Setup Instructions
+
 1. Install [pyenv](https://github.com/pyenv/pyenv) (or [pyenv-win](https://github.com/pyenv-win/pyenv-win) for Windows) for Python version management
 2. Install the latest Python 3.11: `pyenv install 3.11.4`
 3. Install [pipenv](https://github.com/pypa/pipenv) for environment management
 4. Install project requirements: `pipenv install`
 
+# Database
+
+1. Make sure postgres and postgis are installed
+   TODO: Add instructions here
+2. Create a new databse
+   `createdb vacantlotdb`
+3. Connect to the db
+   `psql vacantlotdb`
+4. Enable postgis
+   `CREATE EXTENSION postgis;`
+5. Set your database connection string to an environment variable `VACANT_LOTS_DB`
+
 # Usage Instructions
+
 1. Activate the pipenv shell: `pipenv shell`
 2. Run the main script: `python process_data.py [output_filename]`
 
 # Contribution Instructions
+
 1. Create a fork of this repository and work from branches within your fork. When those changes are ready for review, create a pull request from fork:branch to upstream:main
-2. Before committing changes, format your code to maintain a consistent codebase: 
-    ```
-    pipenv shell
-    black .
-    ```
+2. Before committing changes, format your code to maintain a consistent codebase:
+   ```
+   pipenv shell
+   black .
+   ```
diff --git a/2023_02_02_vacant_lots_layer.ipynb b/archived/2023_02_02_vacant_lots_layer.ipynb
similarity index 100%
rename from 2023_02_02_vacant_lots_layer.ipynb
rename to archived/2023_02_02_vacant_lots_layer.ipynb
diff --git a/constants.py b/archived/constants.py
similarity index 100%
rename from constants.py
rename to archived/constants.py
diff --git a/data_utils.py b/archived/data_utils.py
similarity index 100%
rename from data_utils.py
rename to archived/data_utils.py
diff --git a/process_data.py b/archived/process_data.py
similarity index 100%
rename from process_data.py
rename to archived/process_data.py
diff --git a/notes.md b/notes.md
new file mode 100644
index 00000000..aa015d88
--- /dev/null
+++ b/notes.md
@@ -0,0 +1 @@
+L&I complaints- merge by intersect instead of on address
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/classes/__init__.py b/src/classes/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/script.py b/src/classes/featurelayer.py
similarity index 60%
rename from script.py
rename to src/classes/featurelayer.py
index 18fc9c53..5d3a9dfd 100644
--- a/script.py
+++ b/src/classes/featurelayer.py
@@ -1,12 +1,10 @@
 import requests
 import geopandas as gpd
 import pandas as pd
-import datetime
 from esridump.dumper import EsriDumper
-
-
-# Set the CRS to use for all layers
-use_crs = "EPSG:2272"
+import traceback
+from config.config import USE_CRS, FORCE_RELOAD
+from config.psql import conn
 
 
 class FeatureLayer:
@@ -15,7 +13,14 @@ class FeatureLayer:
     """
 
     def __init__(
-        self, name, esri_rest_urls=None, carto_sql_queries=None, gdf=None, crs=use_crs
+        self,
+        name,
+        esri_rest_urls=None,
+        carto_sql_queries=None,
+        gdf=None,
+        crs=USE_CRS,
+        force_reload=FORCE_RELOAD,
+        from_xy=False,
     ):
         self.name = name
         self.esri_rest_urls = (
@@ -28,6 +33,8 @@ def __init__(
         )
         self.gdf = gdf
         self.crs = crs
+        self.psql_table = name.lower().replace(" ", "_")
+        self.input_crs = "EPSG:4326" if not from_xy else USE_CRS
 
         inputs = [self.esri_rest_urls, self.carto_sql_queries, self.gdf]
         non_none_inputs = [i for i in inputs if i is not None]
@@ -44,10 +51,29 @@ def __init__(
         elif self.gdf is not None:
             self.type = "gdf"
 
-        self.load_data()
+        if force_reload:
+            self.load_data()
+        else:
+            psql_exists = self.check_psql()
+            if not psql_exists:
+                self.load_data()
+
+    def check_psql(self):
+        try:
+            psql_table = gpd.read_postgis(
+                f"SELECT * FROM {self.psql_table}", conn, geom_col="geometry"
+            )
+            if len(psql_table) == 0:
+                return False
+            else:
+                print(f"Loading data for {self.name} from psql...")
+                self.gdf = psql_table
+                return True
+        except:
+            return False
 
     def load_data(self):
-        print(f"Loading data for {self.name}...")
+        print(f"Loading data for {self.name} from {self.type}...")
         if self.type == "gdf":
             self.gdf = gdf
         else:
@@ -65,11 +91,12 @@ def load_data(self):
                             "type": "FeatureCollection",
                             "features": features,
                         }
-                        gdfs.append(
-                            gpd.GeoDataFrame.from_features(
-                                geojson_features, crs=self.crs
-                            )
+
+                        this_gdf = gpd.GeoDataFrame.from_features(
+                            geojson_features, crs=self.input_crs
                         )
+                        this_gdf = this_gdf.to_crs(USE_CRS)
+                        gdfs.append(this_gdf)
 
                     self.gdf = pd.concat(gdfs, ignore_index=True)
 
@@ -87,15 +114,25 @@ def load_data(self):
 
                         data = response.json()["rows"]
                         df = pd.DataFrame(data)
+
                         gdf = gpd.GeoDataFrame(
-                            df, geometry=gpd.points_from_xy(df.x, df.y), crs=self.crs
+                            df,
+                            geometry=gpd.points_from_xy(df.x, df.y),
+                            crs=self.input_crs,
                         )
+                        gdf = gdf.to_crs(USE_CRS)
 
                         gdfs.append(gdf)
                     self.gdf = pd.concat(gdfs, ignore_index=True)
 
+                # save self.gdf to psql
+                self.gdf.to_postgis(
+                    name=self.psql_table, con=conn, if_exists="replace", chunksize=1000
+                )
+
             except Exception as e:
                 print(f"Error loading data for {self.name}: {e}")
+                traceback.print_exc()
                 self.gdf = None
 
     def spatial_join(self, other_layer, how="left", predicate="intersects"):
@@ -104,61 +141,18 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
         They also can create duplicates, so we drop duplicates after the join.
         Note: We may want to revisit the duplicates.
         """
-        self.gdf = gpd.sjoin(self.gdf, other_layer.gdf, how=how, predicate=predicate)
-        self.gdf.drop(columns=["index_right"], inplace=True)
-        self.gdf.drop_duplicates(inplace=True)
-
-
-"""
-Load Vacant Properties Datasets
-"""
-
-vacant_props_layers_to_load = [
-    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/",
-    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/",
-]
-
 
-vacant_properties = FeatureLayer(
-    name="Vacant Properties", esri_rest_urls=vacant_props_layers_to_load
-)
-
-
-"""
-Load City Owned Properties
-"""
-city_owned_properties = FeatureLayer(
-    name="City Owned Properties",
-    esri_rest_urls="https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/",
-)
-
-# Discover which vacant properties are city owned using a spatial join
-vacant_properties.spatial_join(city_owned_properties)
-
-# There are some matches which aren't valid because of imprecise parcel boundaries. Use a match on OPA_ID and OPABRT to remove these.
-vacant_properties.gdf = vacant_properties.gdf.loc[
-    ~(
-        (vacant_properties.gdf["OPABRT"].notnull())
-        & (vacant_properties.gdf["OPA_ID"] != vacant_properties.gdf["OPABRT"])
-    )
-]
-
-# Note: This removes some entries from the dataset, need to revisit this
-
-
-"""
-Load PHS Data
-"""
-
-phs_layers_to_load = [
-    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/",
-    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/",
-]
-
-phs_properties = FeatureLayer(name="PHS Properties", esri_rest_urls=phs_layers_to_load)
+        # If other_layer.gdf isn't a geodataframe, try to make it one
+        if not isinstance(other_layer.gdf, gpd.GeoDataFrame):
+            try:
+                other_layer.gdf = gpd.GeoDataFrame(other_layer.gdf, geometry="geometry")
 
-phs_properties.gdf["COMM_PARTN"] = "PHS"
-phs_properties.gdf = phs_properties.gdf[["COMM_PARTN", "geometry"]]
+            except:
+                print(other_layer.gdf)
+                raise ValueError(
+                    "other_layer.gdf must be a GeoDataFrame or a DataFrame with x and y columns."
+                )
 
-vacant_properties.spatial_join(phs_properties)
-vacant_properties.gdf["COMM_PARTN"].fillna("None", inplace=True)
+        self.gdf = gpd.sjoin(self.gdf, other_layer.gdf, how=how, predicate=predicate)
+        self.gdf.drop(columns=["index_right"], inplace=True)
+        self.gdf.drop_duplicates(inplace=True)
diff --git a/src/config/__init__.py b/src/config/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/config/config.py b/src/config/config.py
new file mode 100644
index 00000000..5ee847d2
--- /dev/null
+++ b/src/config/config.py
@@ -0,0 +1,2 @@
+FORCE_RELOAD = True
+USE_CRS = "EPSG:2272"
diff --git a/src/config/psql.py b/src/config/psql.py
new file mode 100644
index 00000000..5553391d
--- /dev/null
+++ b/src/config/psql.py
@@ -0,0 +1,6 @@
+import os
+from sqlalchemy import create_engine
+
+# Connect to psql
+engine = create_engine(os.environ["VACANT_LOTS_DB"])
+conn = engine.connect()
diff --git a/src/constants/__init__.py b/src/constants/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/constants/services.py b/src/constants/services.py
new file mode 100644
index 00000000..c5ab1d6a
--- /dev/null
+++ b/src/constants/services.py
@@ -0,0 +1,24 @@
+import datetime
+
+VACANT_PROPS_LAYERS_TO_LOAD = [
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/",
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/",
+]
+
+CITY_OWNED_PROPERTIES_TO_LOAD = [
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/LAMAAssets/FeatureServer/0/"
+]
+
+PHS_LAYERS_TO_LOAD = [
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/",
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/",
+]
+
+one_year_ago = (datetime.datetime.now() - datetime.timedelta(days=365)).strftime(
+    "%Y-%m-%d"
+)
+
+# Load data for complaints from L&I
+COMPLAINTS_SQL_QUERY = f"SELECT address, service_request_id, subject, status, service_name, service_code, lat AS y, lon AS x FROM public_cases_fc WHERE requested_datetime >= '{one_year_ago}' AND lat IS NOT NULL;"
+
+VIOLATIONS_SQL_QUERY = f"SELECT parcel_id_num, casenumber, casecreateddate, casetype, casestatus, violationnumber, violationcodetitle, violationstatus, opa_account_num, address, opa_owner, geocode_x AS x, geocode_y AS y FROM violations WHERE violationdate >= '{one_year_ago}' AND geocode_x IS NOT NULL;"
diff --git a/src/data_utils/__init__.py b/src/data_utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/data_utils/city_owned_properties.py b/src/data_utils/city_owned_properties.py
new file mode 100644
index 00000000..5812446c
--- /dev/null
+++ b/src/data_utils/city_owned_properties.py
@@ -0,0 +1,21 @@
+from classes.featurelayer import FeatureLayer
+from constants.services import CITY_OWNED_PROPERTIES_TO_LOAD
+
+
+def city_owned_properties(primary_featurelayer):
+    city_owned_properties = FeatureLayer(
+        name="City Owned Properties",
+        esri_rest_urls=CITY_OWNED_PROPERTIES_TO_LOAD,
+    )
+
+    # Discover which vacant properties are city owned using a spatial join
+    primary_featurelayer.spatial_join(city_owned_properties)
+
+    # There are some matches which aren't valid because of imprecise parcel boundaries. Use a match on OPA_ID and OPABRT to remove these.
+    primary_featurelayer.gdf = primary_featurelayer.gdf.loc[
+        ~(
+            (primary_featurelayer.gdf["OPABRT"].notnull())
+            & (primary_featurelayer.gdf["OPA_ID"] != primary_featurelayer.gdf["OPABRT"])
+        )
+    ]
+    return primary_featurelayer
diff --git a/src/data_utils/l_and_i.py b/src/data_utils/l_and_i.py
new file mode 100644
index 00000000..16389ab3
--- /dev/null
+++ b/src/data_utils/l_and_i.py
@@ -0,0 +1,98 @@
+from classes.featurelayer import FeatureLayer
+from constants.services import COMPLAINTS_SQL_QUERY, VIOLATIONS_SQL_QUERY
+import geopandas as gpd
+
+
+def l_and_i(primary_featurelayer):
+    # Calculate one year ago from today's date
+    l_and_i_complaints = FeatureLayer(
+        name="LI Complaints", carto_sql_queries=COMPLAINTS_SQL_QUERY
+    )
+
+    # filter for only Status = 'Open'
+    l_and_i_complaints.gdf = l_and_i_complaints.gdf[
+        l_and_i_complaints.gdf["status"] == "Open"
+    ]
+
+    # Group by geometry and concatenate the violationcodetitle values into a list with a semicolon separator
+    l_and_i_complaints.gdf = (
+        l_and_i_complaints.gdf.groupby("geometry")["service_name"]
+        .apply(lambda x: "; ".join([val for val in x if val is not None]))
+        .reset_index()
+    )
+    l_and_i_complaints.gdf = gpd.GeoDataFrame(
+        l_and_i_complaints.gdf, geometry="geometry"
+    )
+
+    # collapse complaints_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
+    # l_and_i_complaints.gdf = l_and_i_complaints.gdf.groupby('address')['service_name'].apply(lambda x: '; '.join([val for val in x if val is not None])).reset_index()
+
+    # rename the column to 'li_complaints'
+    l_and_i_complaints.gdf.rename(
+        columns={"service_name": "li_complaints"}, inplace=True
+    )
+
+    # Load data for violations from L&I
+    l_and_i_violations = FeatureLayer(
+        name="LI Violations", carto_sql_queries=VIOLATIONS_SQL_QUERY, from_xy=True
+    )
+
+    all_violations_count_df = (
+        l_and_i_violations.gdf.groupby("opa_account_num")
+        .count()
+        .reset_index()[["opa_account_num", "violationnumber", "geometry"]]
+    )
+    all_violations_count_df = all_violations_count_df.rename(
+        columns={"violationnumber": "all_violations_past_year"}
+    )
+    # filter for only cases where the casestatus is 'IN VIOLATION' or 'UNDER INVESTIGATION'
+    violations_gdf = l_and_i_violations.gdf[
+        (l_and_i_violations.gdf["violationstatus"] == "OPEN")
+    ]
+
+    open_violations_count_df = (
+        violations_gdf.groupby("opa_account_num")
+        .count()
+        .reset_index()[["opa_account_num", "violationnumber", "geometry"]]
+    )
+    open_violations_count_df = open_violations_count_df.rename(
+        columns={"violationnumber": "open_violations_past_year"}
+    )
+    # join the all_violations_count_df and open_violations_count_df dataframes on opa_account_num
+    violations_count_gdf = all_violations_count_df.merge(
+        open_violations_count_df, how="left", on="opa_account_num"
+    )
+
+    # replace NaN values with 0
+    violations_count_gdf.fillna(0, inplace=True)
+
+    # convert the all_violations_past_year and open_violations_past_year columns to integers
+    violations_count_gdf["all_violations_past_year"] = violations_count_gdf[
+        "all_violations_past_year"
+    ].astype(int)
+    violations_count_gdf["open_violations_past_year"] = violations_count_gdf[
+        "open_violations_past_year"
+    ].astype(int)
+
+    # collapse violations_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
+    l_and_i_violations.gdf = (
+        l_and_i_violations.gdf.groupby("geometry")["violationcodetitle"]
+        .apply(lambda x: "; ".join([val for val in x if val is not None]))
+        .reset_index()
+    )
+    l_and_i_complaints.gdf = gpd.GeoDataFrame(
+        l_and_i_complaints.gdf, geometry="geometry"
+    )
+
+    # rename the column to 'li_violations'
+    l_and_i_violations.gdf.rename(
+        columns={"violationcodetitle": "li_code_violations"}, inplace=True
+    )
+
+    # left join the complaints_gdf to the joined_gdf on address
+    primary_featurelayer.spatial_join(l_and_i_violations, predicate="contains")
+
+    # left join the violations_gdf to the joined_gdf on opa_account_num
+    primary_featurelayer.spatial_join(l_and_i_complaints, predicate="contains")
+
+    return primary_featurelayer
diff --git a/src/data_utils/phs_properties.py b/src/data_utils/phs_properties.py
new file mode 100644
index 00000000..58c72713
--- /dev/null
+++ b/src/data_utils/phs_properties.py
@@ -0,0 +1,16 @@
+from classes.featurelayer import FeatureLayer
+from constants.services import PHS_LAYERS_TO_LOAD
+
+
+def phs_properties(primary_featurelayer):
+    phs_properties = FeatureLayer(
+        name="PHS Properties", esri_rest_urls=PHS_LAYERS_TO_LOAD
+    )
+
+    phs_properties.gdf["COMM_PARTN"] = "PHS"
+    phs_properties.gdf = phs_properties.gdf[["COMM_PARTN", "geometry"]]
+
+    primary_featurelayer.spatial_join(phs_properties)
+    primary_featurelayer.gdf["COMM_PARTN"].fillna("None", inplace=True)
+
+    return primary_featurelayer
diff --git a/src/script.py b/src/script.py
new file mode 100644
index 00000000..15a43794
--- /dev/null
+++ b/src/script.py
@@ -0,0 +1,42 @@
+from classes.featurelayer import FeatureLayer
+from config.psql import conn
+from constants.services import VACANT_PROPS_LAYERS_TO_LOAD
+from data_utils.city_owned_properties import city_owned_properties
+from data_utils.phs_properties import phs_properties
+from data_utils.l_and_i import l_and_i
+
+
+"""
+Load Vacant Properties Datasets
+"""
+
+
+vacant_properties = FeatureLayer(
+    name="Vacant Properties", esri_rest_urls=VACANT_PROPS_LAYERS_TO_LOAD
+)
+
+
+"""
+Load City Owned Properties
+"""
+vacant_properties = city_owned_properties(vacant_properties)
+
+
+"""
+Load PHS Data
+"""
+vacant_properties = phs_properties(vacant_properties)
+
+
+"""
+Load L&I Data
+"""
+vacant_properties = l_and_i(vacant_properties)
+
+vacant_properties.gdf.to_postgis(
+    "vacant_properties_end", conn, if_exists="replace", index=False
+)
+
+# Clean up
+
+conn.close()

From d24d56a70ee2e78e29c070baf98f0647e1fc4bde Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Sun, 27 Aug 2023 20:19:41 -0400
Subject: [PATCH 5/7] ready for PR

---
 .gitignore                    |   1 +
 Pipfile.lock                  | 316 +++++++++++++++++++---------------
 README.md                     |   7 +-
 archived/constants.py         |  97 -----------
 archived/data_utils.py        | 248 --------------------------
 archived/process_data.py      | 267 ----------------------------
 notes.md                      |   1 -
 src/classes/featurelayer.py   |  37 ++--
 src/constants/services.py     |   6 +
 src/data_utils/gun_crimes.py  |  80 +++++++++
 src/data_utils/l_and_i.py     |   9 +-
 src/data_utils/rco_geoms.py   |  18 ++
 src/data_utils/tree_canopy.py |  30 ++++
 src/script.py                 |  26 ++-
 14 files changed, 364 insertions(+), 779 deletions(-)
 delete mode 100644 archived/constants.py
 delete mode 100644 archived/data_utils.py
 delete mode 100644 archived/process_data.py
 delete mode 100644 notes.md
 create mode 100644 src/data_utils/gun_crimes.py
 create mode 100644 src/data_utils/rco_geoms.py
 create mode 100644 src/data_utils/tree_canopy.py

diff --git a/.gitignore b/.gitignore
index e2d7d547..05e29933 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 /notebooks/.ipynb_checkpoints/
 .idea
+src/tmp
\ No newline at end of file
diff --git a/Pipfile.lock b/Pipfile.lock
index f614ef52..a25c38c4 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -326,11 +326,11 @@
         },
         "click": {
             "hashes": [
-                "sha256:48ee849951919527a045bfe3bf7baa8a959c423134e1a5b98c05c20ba75a1cbd",
-                "sha256:fa244bb30b3b5ee2cae3da8f55c9e5e0c0e86093306301fb418eb9dc40fbded5"
+                "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
+                "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==8.1.6"
+            "version": "==8.1.7"
         },
         "click-plugins": {
             "hashes": [
@@ -498,43 +498,43 @@
         },
         "fonttools": {
             "hashes": [
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-                "sha256:0df8ef75ba5791e873c9eac2262196497525e3f07699a2576d3ab9ddf41cb619",
-                "sha256:10dac980f2b975ef74532e2a94bb00e97a95b4595fb7f98db493c474d5f54d0e",
-                "sha256:150122ed93127a26bc3670ebab7e2add1e0983d30927733aec327ebf4255b072",
-                "sha256:1f81ed9065b4bd3f4f3ce8e4873cd6a6b3f4e92b1eddefde35d332c6f414acc3",
-                "sha256:27ec3246a088555629f9f0902f7412220c67340553ca91eb540cf247aacb1983",
-                "sha256:2d6dc3fa91414ff4daa195c05f946e6a575bd214821e26d17ca50f74b35b0fe4",
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+                "sha256:be24fcb80493b2c94eae21df70017351851652a37de514de553435b256b2f249",
+                "sha256:c391cd5af88aacaf41dd7cfb96eeedfad297b5899a39e12f4c2c3706d0a3329d",
+                "sha256:c95b0724a6deea2c8c5d3222191783ced0a2f09bd6d33f93e563f6f1a4b3b3a4",
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+                "sha256:db372213d39fa33af667c2aa586a0c1235e88e9c850f5dd5c8e1f17515861868",
+                "sha256:db55cbaea02a20b49fefbd8e9d62bd481aaabe1f2301dabc575acc6b358874fa",
+                "sha256:ed1a13a27f59d1fc1920394a7f596792e9d546c9ca5a044419dca70c37815d7c",
+                "sha256:f2b82f46917d8722e6b5eafeefb4fb585d23babd15d8246c664cd88a5bddd19c",
+                "sha256:f2f806990160d1ce42d287aa419df3ffc42dfefe60d473695fb048355fe0c6a0",
+                "sha256:f720fa82a11c0f9042376fd509b5ed88dab7e3cd602eee63a1af08883b37342b"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.42.0"
+            "version": "==4.42.1"
         },
         "fqdn": {
             "hashes": [
@@ -710,11 +710,11 @@
         },
         "jupyter-server": {
             "hashes": [
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-                "sha256:76b4ae0b568c331acc9aa904fc4a1194ef9bdefa69f232deb3f3cae802528c05"
+                "sha256:98a375347b580e837e7016007c24680a4261ed8ad7cd35196ac087d229f48e5a",
+                "sha256:d64fb4e593907290e5df916e3c9399c15ab2cd7bdb71cbcd1d36452dbfb30523"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.7.1"
+            "version": "==2.7.2"
         },
         "jupyter-server-terminals": {
             "hashes": [
@@ -758,77 +758,113 @@
         },
         "kiwisolver": {
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             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.4.4"
+            "version": "==1.4.5"
         },
         "mapclassify": {
             "hashes": [
@@ -1749,28 +1785,34 @@
         },
         "scipy": {
             "hashes": [
-                "sha256:08d957ca82d3535b3b9ba6c8ff355d78fe975271874e2af267cb5add5bd78625",
-                "sha256:249cfa465c379c9bb2c20123001e151ff5e29b351cbb7f9c91587260602c58d0",
-                "sha256:366a6a937110d80dca4f63b3f5b00cc89d36f678b2d124a01067b154e692bab1",
-                "sha256:39154437654260a52871dfde852adf1b93b1d1bc5dc0ffa70068f16ec0be2624",
-                "sha256:396fae3f8c12ad14c5f3eb40499fd06a6fef8393a6baa352a652ecd51e74e029",
-                "sha256:3b9963798df1d8a52db41a6fc0e6fa65b1c60e85d73da27ae8bb754de4792481",
-                "sha256:3e8eb42db36526b130dfbc417609498a6192381abc1975b91e3eb238e0b41c1a",
-                "sha256:512fdc18c65f76dadaca139348e525646d440220d8d05f6d21965b8d4466bccd",
-                "sha256:aec8c62fbe52914f9cf28d846cf0401dd80ab80788bbab909434eb336ed07c04",
-                "sha256:b41a0f322b4eb51b078cb3441e950ad661ede490c3aca66edef66f4b37ab1877",
-                "sha256:b4bb943010203465ac81efa392e4645265077b4d9e99b66cf3ed33ae12254173",
-                "sha256:b588311875c58d1acd4ef17c983b9f1ab5391755a47c3d70b6bd503a45bfaf71",
-                "sha256:ba94eeef3c9caa4cea7b402a35bb02a5714ee1ee77eb98aca1eed4543beb0f4c",
-                "sha256:be8c962a821957fdde8c4044efdab7a140c13294997a407eaee777acf63cbf0c",
-                "sha256:cce154372f0ebe88556ed06d7b196e9c2e0c13080ecb58d0f35062dc7cc28b47",
-                "sha256:d51565560565a0307ed06fa0ec4c6f21ff094947d4844d6068ed04400c72d0c3",
-                "sha256:e866514bc2d660608447b6ba95c8900d591f2865c07cca0aa4f7ff3c4ca70f30",
-                "sha256:fb5b492fa035334fd249f0973cc79ecad8b09c604b42a127a677b45a9a3d4289",
-                "sha256:ffb28e3fa31b9c376d0fb1f74c1f13911c8c154a760312fbee87a21eb21efe31"
+                "sha256:0f3261f14b767b316d7137c66cc4f33a80ea05841b9c87ad83a726205b901423",
+                "sha256:10eb6af2f751aa3424762948e5352f707b0dece77288206f227864ddf675aca0",
+                "sha256:1342ca385c673208f32472830c10110a9dcd053cf0c4b7d4cd7026d0335a6c1d",
+                "sha256:214cdf04bbae7a54784f8431f976704ed607c4bc69ba0d5d5d6a9df84374df76",
+                "sha256:2b997a5369e2d30c97995dcb29d638701f8000d04df01b8e947f206e5d0ac788",
+                "sha256:2c91cf049ffb5575917f2a01da1da082fd24ed48120d08a6e7297dfcac771dcd",
+                "sha256:3aeb87661de987f8ec56fa6950863994cd427209158255a389fc5aea51fa7055",
+                "sha256:4447ad057d7597476f9862ecbd9285bbf13ba9d73ce25acfa4e4b11c6801b4c9",
+                "sha256:542a757e2a6ec409e71df3d8fd20127afbbacb1c07990cb23c5870c13953d899",
+                "sha256:8d9886f44ef8c9e776cb7527fb01455bf4f4a46c455c4682edc2c2cc8cd78562",
+                "sha256:90d3b1364e751d8214e325c371f0ee0dd38419268bf4888b2ae1040a6b266b2a",
+                "sha256:95763fbda1206bec41157582bea482f50eb3702c85fffcf6d24394b071c0e87a",
+                "sha256:ac74b1512d38718fb6a491c439aa7b3605b96b1ed3be6599c17d49d6c60fca18",
+                "sha256:afdb0d983f6135d50770dd979df50bf1c7f58b5b33e0eb8cf5c73c70600eae1d",
+                "sha256:b0620240ef445b5ddde52460e6bc3483b7c9c750275369379e5f609a1050911c",
+                "sha256:b133f237bd8ba73bad51bc12eb4f2d84cbec999753bf25ba58235e9fc2096d80",
+                "sha256:b29318a5e39bd200ca4381d80b065cdf3076c7d7281c5e36569e99273867f61d",
+                "sha256:b8425fa963a32936c9773ee3ce44a765d8ff67eed5f4ac81dc1e4a819a238ee9",
+                "sha256:d2b813bfbe8dec6a75164523de650bad41f4405d35b0fa24c2c28ae07fcefb20",
+                "sha256:d690e1ca993c8f7ede6d22e5637541217fc6a4d3f78b3672a6fe454dbb7eb9a7",
+                "sha256:e367904a0fec76433bf3fbf3e85bf60dae8e9e585ffd21898ab1085a29a04d16",
+                "sha256:ea932570b1c2a30edafca922345854ff2cd20d43cd9123b6dacfdecebfc1a80b",
+                "sha256:f28f1f6cfeb48339c192efc6275749b2a25a7e49c4d8369a28b6591da02fbc9a",
+                "sha256:f73102f769ee06041a3aa26b5841359b1a93cc364ce45609657751795e8f4a4a",
+                "sha256:fa4909c6c20c3d91480533cddbc0e7c6d849e7d9ded692918c76ce5964997898"
             ],
             "markers": "python_version < '3.13' and python_version >= '3.9'",
-            "version": "==1.11.1"
+            "version": "==1.11.2"
         },
         "send2trash": {
             "hashes": [
@@ -1782,11 +1824,11 @@
         },
         "setuptools": {
             "hashes": [
-                "sha256:d59c97e7b774979a5ccb96388efc9eb65518004537e85d52e81eaee89ab6dd91",
-                "sha256:e13e1b0bc760e9b0127eda042845999b2f913e12437046e663b833aa96d89715"
+                "sha256:3d4dfa6d95f1b101d695a6160a7626e15583af71a5f52176efa5d39a054d475d",
+                "sha256:3d8083eed2d13afc9426f227b24fd1659489ec107c0e86cec2ffdde5c92e790b"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==68.1.0"
+            "version": "==68.1.2"
         },
         "shapely": {
             "hashes": [
@@ -2020,11 +2062,11 @@
         },
         "websocket-client": {
             "hashes": [
-                "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd",
-                "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"
+                "sha256:53e95c826bf800c4c465f50093a8c4ff091c7327023b10bfaff40cf1ef170eaa",
+                "sha256:ce54f419dfae71f4bdba69ebe65bf7f0a93fe71bc009ad3a010aacc3eebad537"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==1.6.1"
+            "markers": "python_version >= '3.8'",
+            "version": "==1.6.2"
         },
         "widgetsnbextension": {
             "hashes": [
diff --git a/README.md b/README.md
index 4aa9406b..22653a2f 100644
--- a/README.md
+++ b/README.md
@@ -21,10 +21,15 @@ Gun crime x vacant lots mapping tool layer
    `CREATE EXTENSION postgis;`
 5. Set your database connection string to an environment variable `VACANT_LOTS_DB`
 
+# awkde
+
+We are using the [awkde package](https://github.com/mennthor/awkde) to create the Adaptive Width KDE. It is not available through pip. Install directly in the `src` folder using their instructions.
+
 # Usage Instructions
 
 1. Activate the pipenv shell: `pipenv shell`
-2. Run the main script: `python process_data.py [output_filename]`
+2. Move the to src folder `cd src`
+3. Run the main script `python script.py`
 
 # Contribution Instructions
 
diff --git a/archived/constants.py b/archived/constants.py
deleted file mode 100644
index c31082d6..00000000
--- a/archived/constants.py
+++ /dev/null
@@ -1,97 +0,0 @@
-ARCGIS_BASE_URL: str = "https://services.arcgis.com"
-
-ARCGIS_LAND_DATASET_PATH: str = "/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Land/FeatureServer/0/query"
-ARCGIS_BUILDINGS_DATASET_PATH: str = "/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Vacant_Indicators_Bldg/FeatureServer/0/query"
-ARCGIS_PHS_LANDCARE_DATASET_PATH: str = (
-    "/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_CommunityLandcare/FeatureServer/0/query"
-)
-ARCGIS_PHS_MAINTENANCE_DATASET_PATH: str = "/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/query"
-ARCGIS_COMMUNITY_ORGANISATIONS_DATASET_PATH: str = (
-    "/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Zoning_RCO/FeatureServer/0/query"
-)
-
-PHILADELPHIA_CARTO_BASE_URL: str = "https://phl.carto.com/api/v2/sql"
-
-PHILADELPHIA_NEIGHBORHOODS_DATASET_URL: str = "https://github.com/azavea/geo-data/raw/master/Neighborhoods_Philadelphia/Neighborhoods_Philadelphia.zip"
-
-PHILADELPHIA_TREE_CANOPY_DATASET_URL: str = (
-    "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/pa.zip.zip"
-)
-
-DEFAULT_ARCGIS_QUERY_PARAMETERS: dict = {
-    "where": "1=1",
-    "outFields": "*",
-    "returnGeometry": "true",
-    "f": "json",
-    "resultRecordCount": 2000,
-}
-
-PUBLIC_PROPERTY_OWNER_VALUES = [
-    "PHILADELPHIA HOUSING DEVELOPMENT CORPORATION",
-    "REDEVELOPMENT AUTHORITY OF PHILADELPHIA",
-    "PHILA REDEVELOPMENT AUTHO",
-    "COMMONWEALTH OF PENNA",
-    "DEPT OF PUBLIC PROP; CITY OF PHILA",
-    "PHILA HOUSING AUTHORITY",
-    "PHILADELPHIA HOUSING AUTH",
-    "PUBLIC PROP DIV; CITY OF PHILA",
-    "COMMONWEALTH OF PA",
-    "REAL ESTATE DIV; CITY OF PHILA",
-    "PHILADELPHIA HOUSING AUTHORITY",
-    "PHILADELPHIA REDEVELOPMENT AUTH",
-    "DEVELOPMENT CORPORATION; PHILADELPHIA HOUSING",
-    "DEPT OF PUB PROP; CITY OF PHILA",
-    "URBAN DEVELOPMENT; SECRETARY OF HOUSINGURBAN DEVELOPMENT; SECRETARY OF HOUSING AND",
-    "PHILADELPHIA LAND BANK",
-    "PUBLIC PROP REAL ESTATE; CITY OF PHILA",
-    "PENNDOT",
-    "REAL ESTATE DIVISION; CITY OF PHILA",
-    "PHILADELPHIA LAND INVESTM",
-    "DEPT OF PUBLIC PROP; CITY OF PHILADELPHIA",
-    "REDEVELOPMENT AUTHORITY OF PHILA",
-    "CITY OF PHILADELPHIA DEPARTMENT OF PUBLIC PROPERTY",
-    "DEPT OF PUBLC PROP; CITY OF PHILA",
-    "PHILA HOUSING DEV CORP",
-    "PHILA REDEVELOPMENT AUTH",
-    "KENSINGTON HOUSING AUTHOR",
-    "REDEVELOPMENT AUTHORITY O",
-    "PHILADELPHIA LAND INVESTMENT",
-    "PHILADELPHIA REDEVELOPMEN",
-    "COMMONWEALTH OF PENNSYLVANIA",
-    "DEP OF PUB PROP; CITY OF PHILA",
-    "SECRETARY OF HOUSING AND URBAN DEVELOPMENT",
-    "PHILADELPHIA REDEVELOPMENT AUTHORITY",
-    "CITY OF PHILADELPHIA",
-    "KENSINGTON HOUSING AUTHORITY",
-    "DEPT PUBLIC PROP R E DIV; CITY OF PHILA",
-    "REDEVELOPMENT AUTHORITY",
-    "CITY OF PHILA",
-    "DEPT PUB PROP; CITY OF PHILA",
-]
-
-REDEVELOPMENT_OWNER_VALUES = [
-    "REDEVELOPMENT AUTHORITY OF PHILA",
-    "PHILA REDEVELOPMENT AUTH",
-    "REDEVELOPMENT AUTHORITY O",
-    "PHILADELPHIA REDEVELOPMEN",
-    "PHILA REDEVELOPMENT AUTHO",
-    "REDEVELOPMENT AUTHORITY",
-    "REDEVELOPMENT AUTH",
-]
-
-DEPARTMENT_OF_PUBLIC_PROPERTY_OWNER_VALUES = [
-    "DEPT OF PUBLIC PROPERTY",
-    "DEPT OF PUBLIC PROPERT",
-    "DEPT OF PUBLC PROP; CITY OF " "PHILA",
-    "DEPT OF PUBLIC PROP; CITY OF PHILA",
-    "DEPT OF PUBLIC PROPERTY; CITY OF PHILA",
-    "DEPT PUB PROP; CITY OF PHILA",
-    "DEPT OF PUB PROP; CITY OF PHILA",
-    "DEP OF PUB PROP; CITY OF PHILA",
-    "DEPT OF PUBLIC PROP; CITY OF PHILADELPHIA",
-    "DEPT PUBLIC PROP R E DIV; CITY OF PHILA",
-    "PUBLIC PROP DIV; CITY OF PHILA",
-    "PUBLIC PROP REAL ESTATE; CITY OF PHILA",
-    "REAL ESTATE DIV; CITY OF PHILA",
-    "REAL ESTATE DIVISION; CITY OF PHILA",
-]
diff --git a/archived/data_utils.py b/archived/data_utils.py
deleted file mode 100644
index 82ddcb40..00000000
--- a/archived/data_utils.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import datetime
-import io
-import warnings
-
-import fiona
-import geopandas as gpd
-import pandas as pd
-import requests
-from shapely import Polygon
-
-from constants import (
-    DEFAULT_ARCGIS_QUERY_PARAMETERS,
-    ARCGIS_BASE_URL,
-    PHILADELPHIA_CARTO_BASE_URL,
-    REDEVELOPMENT_OWNER_VALUES,
-    DEPARTMENT_OF_PUBLIC_PROPERTY_OWNER_VALUES,
-    PUBLIC_PROPERTY_OWNER_VALUES,
-)
-
-warnings.filterwarnings("ignore", "GeoSeries.notna", UserWarning)
-
-
-def get_arcgis_dataset(dataset_path: str) -> gpd.GeoDataFrame:
-    dfs: list[pd.DataFrame] = []
-    pagination_offset: int = 0
-
-    while True:
-        query_params = DEFAULT_ARCGIS_QUERY_PARAMETERS | {
-            "resultOffset": pagination_offset
-        }
-        response = requests.get(url=ARCGIS_BASE_URL + dataset_path, params=query_params)
-        if response.status_code != 200:
-            print(
-                f"Error calling ArcGIS API, request returned HTTP {response.status_code}"
-            )
-            break
-
-        df = pd.DataFrame(response.json()["features"])
-
-        # separate the attributes column into one column per attribute
-        df = pd.concat(
-            [df.drop(["attributes"], axis=1), df["attributes"].apply(pd.Series)], axis=1
-        )
-
-        # make the `geometry` column a shapely geometry object
-        df["geometry"] = df["geometry"].apply(lambda x: Polygon(x["rings"][0]))
-
-        land_gdf = gpd.GeoDataFrame(df, geometry="geometry", crs="EPSG:3857")
-        dfs.append(land_gdf.to_crs(epsg=2272))
-
-        pagination_offset += len(land_gdf)
-
-        # we specify 2000 results in the query, <2000 indicates no further results
-        if len(land_gdf) < 2000:
-            break
-
-    return gpd.GeoDataFrame(pd.concat(dfs, ignore_index=True), crs="EPSG:2272")
-
-
-def get_philadelphia_li_complaint_dataset() -> gpd.GeoDataFrame:
-    one_year_ago: str = (
-        datetime.datetime.now() - datetime.timedelta(days=365)
-    ).strftime("%Y-%m-%d")
-
-    query: str = (
-        f"SELECT address, service_request_id, subject, status, service_name, service_code, lat, lon"
-        f" FROM public_cases_fc "
-        f"WHERE requested_datetime >= '{one_year_ago}'"
-    )
-
-    response = requests.get(url=PHILADELPHIA_CARTO_BASE_URL, params={"q": query})
-
-    df = pd.DataFrame(response.json()["rows"])
-    return gpd.GeoDataFrame(
-        df, geometry=gpd.points_from_xy(df.lon, df.lat), crs="EPSG:2272"
-    )
-
-
-def get_philadelphia_li_violation_dataset() -> gpd.GeoDataFrame:
-    one_year_ago: str = (
-        datetime.datetime.now() - datetime.timedelta(days=365)
-    ).strftime("%Y-%m-%d")
-
-    query: str = (
-        f"SELECT parcel_id_num, casenumber, casecreateddate, casetype, casestatus, violationnumber,"
-        f"       violationcodetitle, violationstatus, opa_account_num, address, opa_owner, "
-        f"       geocode_x, geocode_y "
-        f"FROM violations "
-        f"WHERE violationdate >= '{one_year_ago}'"
-    )
-
-    response = requests.get(url=PHILADELPHIA_CARTO_BASE_URL, params={"q": query})
-
-    df = pd.DataFrame(response.json()["rows"])
-
-    return gpd.GeoDataFrame(
-        df,
-        geometry=gpd.points_from_xy(df.geocode_x, df.geocode_y),
-        crs="EPSG:2272",
-    )
-
-
-def get_philadelphia_gun_crime_dataset() -> gpd.GeoDataFrame:
-    one_year_ago: str = (
-        datetime.datetime.now() - datetime.timedelta(days=365)
-    ).strftime("%Y-%m-%d")
-
-    query = (
-        f"SELECT text_general_code, dispatch_date, point_x, point_y "
-        f"FROM incidents_part1_part2 "
-        f"WHERE dispatch_date_time >= '{one_year_ago}' "
-        f"AND text_general_code IN ('Aggravated Assault Firearm', 'Robbery Firearm')"
-    )
-
-    response = requests.get(url=PHILADELPHIA_CARTO_BASE_URL, params={"q": query})
-
-    df = pd.DataFrame(response.json()["rows"])
-
-    gdf = gpd.GeoDataFrame(
-        df, geometry=gpd.points_from_xy(df.point_x, df.point_y), crs="EPSG:4326"
-    )
-
-    gdf.drop(["point_x", "point_y"], axis=1, inplace=True)
-
-    gdf.to_crs(epsg=2272, inplace=True)
-
-    return gdf[gdf["geometry"].notnull()]
-
-
-def get_property_assessment_dataset() -> pd.DataFrame:
-    query = "SELECT parcel_number, market_value " "FROM opa_properties_public"
-
-    response = requests.get(url=PHILADELPHIA_CARTO_BASE_URL, params={"q": query})
-
-    return pd.DataFrame(response.json()["rows"])
-
-
-def get_delinquency_dataset() -> pd.DataFrame:
-    query = "SELECT * FROM real_estate_tax_delinquencies"
-
-    response = requests.get(url=PHILADELPHIA_CARTO_BASE_URL, params={"q": query})
-
-    return pd.DataFrame(response.json()["rows"])
-
-
-def get_shapefile_dataset(dataset_url: str) -> gpd.GeoDataFrame:
-    response = requests.get(url=dataset_url)
-
-    bytes_array = io.BytesIO(response.content).read()
-
-    with fiona.BytesCollection(bytes_array) as src:
-        crs = src.crs
-        gdf = gpd.GeoDataFrame.from_features(src, crs=crs)
-
-    return gdf
-
-
-def starts_with_preposition(string):
-    return string.split(" ")[0].lower() in [
-        "a",
-        "an",
-        "and",
-        "as",
-        "at",
-        "but",
-        "by",
-        "for",
-        "from",
-        "in",
-        "into",
-        "nor",
-        "of",
-        "on",
-        "or",
-        "so",
-        "the",
-        "to",
-        "up",
-        "yet",
-    ]
-
-
-def combine_owners(row):
-    if pd.isnull(row["OWNER1"]) and pd.isnull(row["OWNER2"]):
-        return None
-    elif pd.isnull(row["OWNER1"]) and not pd.isnull(row["OWNER2"]):
-        return row["OWNER2"]
-    elif not pd.isnull(row["OWNER1"]) and pd.isnull(row["OWNER2"]):
-        return row["OWNER1"]
-    elif starts_with_preposition(row["OWNER2"]):
-        return row["OWNER1"] + " " + row["OWNER2"]
-    else:
-        return row["OWNER2"] + "; " + row["OWNER1"]
-
-
-def clean_ownership_values(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
-    """
-    correct misspellings, and identify public vs. private ownership
-    """
-    gdf["OWNER"] = gdf.apply(combine_owners, axis=1)
-
-    gdf.loc[
-        gdf["OWNER"] == "PHILADELPHIA HOUSING AUTH", "OWNER"
-    ] = "PHILADELPHIA HOUSING AUTHORITY"
-    gdf.loc[
-        gdf["OWNER"] == "PHILA HOUSING AUTHORITY", "OWNER"
-    ] = "PHILADELPHIA HOUSING AUTHORITY"
-
-    for val in REDEVELOPMENT_OWNER_VALUES:
-        gdf.loc[
-            gdf["OWNER"] == val, "OWNER"
-        ] = "REDEVELOPMENT AUTHORITY OF PHILADELPHIA"
-
-    for var in DEPARTMENT_OF_PUBLIC_PROPERTY_OWNER_VALUES:
-        gdf.loc[
-            gdf["OWNER"] == var, "OWNER"
-        ] = "CITY OF PHILADELPHIA DEPARTMENT OF PUBLIC PROPERTY"
-
-    gdf.loc[
-        gdf["OWNER"] == "URBAN DEVELOPMENT; SECRETARY OF HOUSING", "OWNER"
-    ] = "SECRETARY OF HOUSING AND URBAN DEVELOPMENT"
-    gdf.loc[
-        gdf["OWNER"] == "URBAN DEVELOPMENT; SECRETARY OF HOUSING AND", "OWNER"
-    ] = "SECRETARY OF HOUSING AND URBAN DEVELOPMENT"
-
-    gdf.loc[
-        gdf["OWNER"] == "COMMONWEALTH OF PA", "OWNER"
-    ] = "COMMONWEALTH OF PENNSYLVANIA"
-    gdf.loc[
-        gdf["OWNER"] == "COMMONWEALTH OF PENNA", "OWNER"
-    ] = "COMMONWEALTH OF PENNSYLVANIA"
-
-    gdf.loc[
-        gdf["OWNER"] == "DEVELOPMENT CORPORATION; PHILADELPHIA HOUSING", "OWNER"
-    ] = "PHILADELPHIA HOUSING DEVELOPMENT CORPORATION"
-    gdf.loc[
-        gdf["OWNER"] == "PHILA HOUSING DEV CORP", "OWNER"
-    ] = "PHILADELPHIA HOUSING DEVELOPMENT CORPORATION"
-
-    gdf.loc[
-        gdf["OWNER"] == "DEPARTMENT OF TRANSPORTAT; COMMONWEALTH OF PENNSYLVA", "OWNER"
-    ] = "PENNDOT"
-
-    gdf.loc[gdf["OWNER"] == "CITY OF PHILADELPHIA", "OWNER"] = "CITY OF PHILA"
-
-    gdf["public_owner"] = gdf["OWNER"].isin(PUBLIC_PROPERTY_OWNER_VALUES)
-
-    return gdf.drop(["OWNER1", "OWNER2"], axis=1)
diff --git a/archived/process_data.py b/archived/process_data.py
deleted file mode 100644
index 106da7b4..00000000
--- a/archived/process_data.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import argparse
-import os
-
-import geopandas as gpd
-import pandas as pd
-import logging
-
-from constants import (
-    ARCGIS_LAND_DATASET_PATH,
-    ARCGIS_BUILDINGS_DATASET_PATH,
-    ARCGIS_PHS_LANDCARE_DATASET_PATH,
-    ARCGIS_PHS_MAINTENANCE_DATASET_PATH,
-    ARCGIS_COMMUNITY_ORGANISATIONS_DATASET_PATH,
-    PHILADELPHIA_TREE_CANOPY_DATASET_URL,
-    PHILADELPHIA_NEIGHBORHOODS_DATASET_URL,
-)
-from data_utils import (
-    get_arcgis_dataset,
-    get_philadelphia_li_complaint_dataset,
-    get_philadelphia_li_violation_dataset,
-    get_shapefile_dataset,
-    get_philadelphia_gun_crime_dataset,
-    get_property_assessment_dataset,
-    get_delinquency_dataset,
-    clean_ownership_values,
-)
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(
-    level=os.environ.get("LOGLEVEL", "INFO"), format="%(asctime)s %(message)s"
-)
-
-
-def clean_and_merge_gdfs(
-    land_gdf,
-    buildings_gdf,
-    phs_landcare_gdf,
-    phs_maintenance_gdf,
-    community_orgs_gdf,
-    complaints_gdf,
-    violations_gdf,
-    neighborhoods_gdf,
-    tree_canopy_gdf,
-    gun_crime_gdf,
-    property_assessment_df,
-    delinquency_df,
-) -> gpd.GeoDataFrame:
-    """this method needs quite a bit of refactoring"""
-
-    """ land & buildings """
-    columns = [
-        "geometry",
-        "ADDRESS",
-        "BLDG_DESC",
-        "OPA_ID",
-        "COUNCILDISTRICT",
-        "ZIPCODE",
-        "OWNER1",
-        "OWNER2",
-    ]
-
-    land_gdf, buildings_gdf = land_gdf[columns], buildings_gdf[columns]
-    land_gdf.insert(0, "type", "lot")
-    buildings_gdf.insert(0, "type", "building")
-
-    main_gdf = pd.concat([land_gdf, buildings_gdf], axis=0)
-    main_gdf = clean_ownership_values(main_gdf)
-
-    """ PHS """
-    phs_landcare_gdf = phs_landcare_gdf[["geometry", "COMM_PARTN"]]
-    phs_maintenance_gdf.insert(0, "COMM_PARTN", "PHS")
-    phs_maintenance_gdf = phs_maintenance_gdf[["geometry", "COMM_PARTN"]]
-
-    phs_gdf = gpd.GeoDataFrame(
-        pd.concat([phs_landcare_gdf, phs_maintenance_gdf], ignore_index=True),
-        crs=phs_landcare_gdf.crs,
-    )
-
-    main_gdf = gpd.sjoin(main_gdf, phs_gdf, how="left", predicate="intersects")
-    main_gdf.drop(["index_right"], axis=1, inplace=True)
-
-    main_gdf["COMM_PARTN"] = main_gdf["COMM_PARTN"].fillna("None", inplace=True)
-
-    """ L&I """
-    complaints_gdf.drop(["lat", "lon"], axis=1, inplace=True)
-
-    complaints_gdf = complaints_gdf[complaints_gdf["status"] == "Open"]
-
-    complaints_gdf = (
-        complaints_gdf.groupby("address")["service_name"]
-        .apply(lambda x: "; ".join([val for val in x if val is not None]))
-        .reset_index()
-    )
-
-    complaints_gdf.rename(columns={"service_name": "li_complaints"}, inplace=True)
-
-    all_violations_count_df = (
-        violations_gdf.groupby("opa_account_num")
-        .count()
-        .reset_index()[["opa_account_num", "violationnumber"]]
-    )
-    all_violations_count_df = all_violations_count_df.rename(
-        columns={"violationnumber": "all_violations_past_year"}
-    )
-
-    violations_gdf = violations_gdf[(violations_gdf["violationstatus"] == "OPEN")]
-    open_violations_count_df = (
-        violations_gdf.groupby("opa_account_num")
-        .count()
-        .reset_index()[["opa_account_num", "violationnumber"]]
-    )
-    open_violations_count_df = open_violations_count_df.rename(
-        columns={"violationnumber": "open_violations_past_year"}
-    )
-
-    violations_count_gdf = all_violations_count_df.merge(
-        open_violations_count_df, how="left", on="opa_account_num"
-    )
-    violations_count_gdf.fillna(0, inplace=True)
-
-    violations_count_gdf["all_violations_past_year"] = violations_count_gdf[
-        "all_violations_past_year"
-    ].astype(int)
-    violations_count_gdf["open_violations_past_year"] = violations_count_gdf[
-        "open_violations_past_year"
-    ].astype(int)
-
-    violations_gdf = (
-        violations_gdf.groupby("opa_account_num")["violationcodetitle"]
-        .apply(lambda x: "; ".join([val for val in x if val is not None]))
-        .reset_index()
-    )
-    violations_gdf.rename(
-        columns={"violationcodetitle": "li_code_violations"}, inplace=True
-    )
-
-    main_gdf = main_gdf.merge(
-        complaints_gdf, how="left", left_on="ADDRESS", right_on="address"
-    )
-    main_gdf = main_gdf.merge(
-        violations_gdf, how="left", left_on="OPA_ID", right_on="opa_account_num"
-    )
-
-    main_gdf.drop(["address", "opa_account_num"], axis=1, inplace=True)
-
-    """ Centroids """
-    main_gdf["centroid"] = main_gdf["geometry"].centroid
-    poly_gdf = main_gdf[["OPA_ID", "geometry"]]
-    main_gdf.drop(["geometry"], axis=1, inplace=True)
-    main_gdf.set_geometry("centroid", inplace=True)
-
-    """ neighborhoods """
-    main_gdf = gpd.sjoin(
-        main_gdf, neighborhoods_gdf, how="left", predicate="intersects"
-    )
-    main_gdf = main_gdf.drop(
-        ["index_right", "NAME", "LISTNAME", "Shape_Leng", "Shape_Area"], axis=1
-    )
-    main_gdf.rename(columns={"MAPNAME": "neighborhood"}, inplace=True)
-
-    """ community orgs """
-    community_orgs_gdf["PRIMARY_PHONE"] = community_orgs_gdf["PRIMARY_PHONE"].astype(
-        str
-    )
-    community_orgs_gdf["EXPIRATIONYEAR"] = community_orgs_gdf["EXPIRATIONYEAR"].astype(
-        str
-    )
-    rco_aggregate_cols = [
-        "ORGANIZATION_NAME",
-        "ORGANIZATION_ADDRESS",
-        "PRIMARY_EMAIL",
-        "PRIMARY_PHONE",
-    ]
-    community_orgs_gdf["rco_info"] = community_orgs_gdf[rco_aggregate_cols].agg(
-        "; ".join, axis=1
-    )
-    rcos_final_cols = ["geometry", "rco_info"]
-    community_orgs_gdf = community_orgs_gdf[rcos_final_cols]
-
-    w_community_orgs_gdf = gpd.sjoin(
-        main_gdf, community_orgs_gdf, how="left", predicate="within"
-    )
-    w_community_orgs_gdf.drop(["index_right"], axis=1, inplace=True)
-    w_community_orgs_gdf.rename(columns={"rco_info": "rco_info"}, inplace=True)
-    w_community_orgs_gdf.drop_duplicates(subset="OPA_ID", inplace=True)
-
-    w_community_orgs_gdf["OPA_ID"] = w_community_orgs_gdf["OPA_ID"].astype(str)
-
-    rcos_by_opa_id_gdf = (
-        w_community_orgs_gdf.groupby("OPA_ID")["rco_info"]
-        .apply(lambda x: "| ".join([str(val) for val in x if val is not None]))
-        .reset_index()
-    )
-
-    rcos_by_opa_id_gdf.rename(columns={"rco_info": "relevant_rcos"}, inplace=True)
-
-    main_gdf = main_gdf.merge(
-        rcos_by_opa_id_gdf, how="left", left_on="OPA_ID", right_on="OPA_ID"
-    )
-    main_gdf.drop_duplicates(subset="OPA_ID", inplace=True)
-
-    """ tree canopy """
-    main_gdf = gpd.sjoin(main_gdf, tree_canopy_gdf, how="left", predicate="intersects")
-    main_gdf.drop(["index_right"], axis=1, inplace=True)
-    main_gdf.drop_duplicates(subset="OPA_ID", inplace=True)
-
-    return main_gdf
-
-
-def orchestrate_processing(output_filename: str) -> bool:
-    logger.info("Fetching data sources")
-    land_gdf = get_arcgis_dataset(ARCGIS_LAND_DATASET_PATH)
-    buildings_gdf = get_arcgis_dataset(ARCGIS_BUILDINGS_DATASET_PATH)
-    phs_landcare_gdf = get_arcgis_dataset(ARCGIS_PHS_LANDCARE_DATASET_PATH)
-    phs_maintenance_gdf = get_arcgis_dataset(ARCGIS_PHS_MAINTENANCE_DATASET_PATH)
-    community_orgs_gdf = get_arcgis_dataset(ARCGIS_COMMUNITY_ORGANISATIONS_DATASET_PATH)
-    complaints_gdf = get_philadelphia_li_complaint_dataset()
-    violations_gdf = get_philadelphia_li_violation_dataset()
-    neighborhoods_gdf = get_shapefile_dataset(PHILADELPHIA_NEIGHBORHOODS_DATASET_URL)
-    tree_canopy_gdf = get_shapefile_dataset(PHILADELPHIA_TREE_CANOPY_DATASET_URL)
-    gun_crime_gdf = get_philadelphia_gun_crime_dataset()
-    property_assessment_df = get_property_assessment_dataset()
-    delinquency_df = get_delinquency_dataset()
-    logger.info("Successfully fetched data")
-
-    logger.info("Cleaning and merging the data")
-    main_gdf = clean_and_merge_gdfs(
-        land_gdf,
-        buildings_gdf,
-        phs_landcare_gdf,
-        phs_maintenance_gdf,
-        community_orgs_gdf,
-        complaints_gdf,
-        violations_gdf,
-        neighborhoods_gdf,
-        tree_canopy_gdf,
-        gun_crime_gdf,
-        property_assessment_df,
-        delinquency_df,
-    )
-    logger.info("Successfully processed data")
-
-    logger.info(f"Writing data to ./{output_filename}")
-    main_gdf.to_file(f"./{output_filename}", driver="GeoJSON")
-
-    return True
-
-
-def main():
-    parser: argparse.ArgumentParser = argparse.ArgumentParser(
-        description="Process various Philadelphia data into a GeoJSON file"
-    )
-    parser.add_argument("filename", help="the output file name")
-    args = parser.parse_args()
-
-    output_filename: str = args.filename
-
-    if orchestrate_processing(output_filename):
-        logger.info(
-            f"Successfully processed data. Results are saved in {output_filename}"
-        )
-    else:
-        logger.info("An error occurred during data processing.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/notes.md b/notes.md
deleted file mode 100644
index aa015d88..00000000
--- a/notes.md
+++ /dev/null
@@ -1 +0,0 @@
-L&I complaints- merge by intersect instead of on address
diff --git a/src/classes/featurelayer.py b/src/classes/featurelayer.py
index 5d3a9dfd..fb87d6d9 100644
--- a/src/classes/featurelayer.py
+++ b/src/classes/featurelayer.py
@@ -39,24 +39,22 @@ def __init__(
         inputs = [self.esri_rest_urls, self.carto_sql_queries, self.gdf]
         non_none_inputs = [i for i in inputs if i is not None]
 
-        if len(non_none_inputs) != 1:
-            raise ValueError(
-                "Exactly one of esri_rest_urls, carto_sql_queries, or gdf must be provided."
-            )
-
-        if self.esri_rest_urls is not None:
-            self.type = "esri"
-        elif self.carto_sql_queries is not None:
-            self.type = "carto"
-        elif self.gdf is not None:
-            self.type = "gdf"
-
-        if force_reload:
-            self.load_data()
-        else:
-            psql_exists = self.check_psql()
-            if not psql_exists:
+        if len(non_none_inputs) > 0:
+            if self.esri_rest_urls is not None:
+                self.type = "esri"
+            elif self.carto_sql_queries is not None:
+                self.type = "carto"
+            elif self.gdf is not None:
+                self.type = "gdf"
+
+            if force_reload:
                 self.load_data()
+            else:
+                psql_exists = self.check_psql()
+                if not psql_exists:
+                    self.load_data()
+        else:
+            print('Initialized FeatureLayer with no data.')
 
     def check_psql(self):
         try:
@@ -145,7 +143,7 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
         # If other_layer.gdf isn't a geodataframe, try to make it one
         if not isinstance(other_layer.gdf, gpd.GeoDataFrame):
             try:
-                other_layer.gdf = gpd.GeoDataFrame(other_layer.gdf, geometry="geometry")
+                other_layer.rebuild_gdf()
 
             except:
                 print(other_layer.gdf)
@@ -156,3 +154,6 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
         self.gdf = gpd.sjoin(self.gdf, other_layer.gdf, how=how, predicate=predicate)
         self.gdf.drop(columns=["index_right"], inplace=True)
         self.gdf.drop_duplicates(inplace=True)
+
+    def rebuild_gdf(self):
+        self.gdf = gpd.GeoDataFrame(self.gdf, geometry="geometry")
\ No newline at end of file
diff --git a/src/constants/services.py b/src/constants/services.py
index c5ab1d6a..3c87684a 100644
--- a/src/constants/services.py
+++ b/src/constants/services.py
@@ -14,6 +14,10 @@
     "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/PHS_PhilaLandCare_Maintenance/FeatureServer/0/",
 ]
 
+RCOS_LAYERS_TO_LOAD = [
+    "https://services.arcgis.com/fLeGjb7u4uXqeF9q/ArcGIS/rest/services/Zoning_RCO/FeatureServer/0/"
+]
+
 one_year_ago = (datetime.datetime.now() - datetime.timedelta(days=365)).strftime(
     "%Y-%m-%d"
 )
@@ -22,3 +26,5 @@
 COMPLAINTS_SQL_QUERY = f"SELECT address, service_request_id, subject, status, service_name, service_code, lat AS y, lon AS x FROM public_cases_fc WHERE requested_datetime >= '{one_year_ago}' AND lat IS NOT NULL;"
 
 VIOLATIONS_SQL_QUERY = f"SELECT parcel_id_num, casenumber, casecreateddate, casetype, casestatus, violationnumber, violationcodetitle, violationstatus, opa_account_num, address, opa_owner, geocode_x AS x, geocode_y AS y FROM violations WHERE violationdate >= '{one_year_ago}' AND geocode_x IS NOT NULL;"
+
+GUNCRIME_SQL_QUERY = f"SELECT text_general_code, dispatch_date, point_x AS x, point_y AS y FROM incidents_part1_part2 WHERE dispatch_date_time >= '{one_year_ago}' AND text_general_code IN ('Aggravated Assault Firearm', 'Robbery Firearm') AND point_x IS NOT NULL;"
diff --git a/src/data_utils/gun_crimes.py b/src/data_utils/gun_crimes.py
new file mode 100644
index 00000000..4fb2ffcc
--- /dev/null
+++ b/src/data_utils/gun_crimes.py
@@ -0,0 +1,80 @@
+from constants.services import GUNCRIME_SQL_QUERY
+from config.config import USE_CRS
+from classes.featurelayer import FeatureLayer
+import numpy as np
+import matplotlib.pyplot as plt
+from awkde.awkde import GaussianKDE
+import rasterio
+from rasterio.transform import Affine
+import mapclassify
+
+def gun_crimes(primary_featurelayer):
+    # Initialize gun_crimes object
+    gun_crimes = FeatureLayer(name="Gun Crimes", carto_sql_queries=GUNCRIME_SQL_QUERY)
+
+    # Extract x, y coordinates from geometry
+    x = np.array([])
+    y = np.array([])
+
+    for geom in gun_crimes.gdf.geometry:
+        coords = np.array(geom.xy)
+        x = np.concatenate([x, coords[0]])
+        y = np.concatenate([y, coords[1]])
+
+    # Prepare data for KDE
+    X = np.array(list(zip(x, y)))
+
+    # Generate grid for plotting
+    x_grid, y_grid = np.linspace(x.min(), x.max(), 1000), np.linspace(y.min(), y.max(), 1000)
+    xx, yy = np.meshgrid(x_grid, y_grid)
+    grid_points = np.array([xx.ravel(), yy.ravel()]).T
+
+    # Compute adaptive KDE values
+    kde = GaussianKDE(glob_bw="silverman", alpha=0.999, diag_cov=True)
+    kde.fit(X)
+
+    z = kde.predict(grid_points)
+    zz = z.reshape(xx.shape)
+
+    # Calculate resolutions and min values
+    x_res = (x.max() - x.min()) / (len(x_grid) - 1)
+    y_res = (y.max() - y.min()) / (len(y_grid) - 1)
+    min_x, min_y = x.min(), y.min()
+
+    # Save the plot in tmp folder
+    plt.pcolormesh(xx, yy, zz)
+    plt.scatter(x, y, c='red', s=0.005)
+    plt.colorbar()
+    plt.savefig('tmp/kde.png')
+
+
+    # Define the affine transform
+    transform = Affine.translation(min_x, min_y) * Affine.scale(x_res, y_res)
+
+    # Export as raster
+    with rasterio.open('tmp/output.tif', 'w', driver='GTiff',
+                    height=zz.shape[0],
+                    width=zz.shape[1],
+                    count=1, dtype=zz.dtype,
+                    crs=USE_CRS,
+                    transform=transform) as dst:
+        dst.write(zz, 1)
+    
+    primary_featurelayer.gdf['centroid'] = primary_featurelayer.gdf.geometry.centroid
+    
+    coord_list = [(x,y) for x,y in zip(primary_featurelayer.gdf['centroid'].x, primary_featurelayer.gdf['centroid'].y)]
+
+    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=['centroid'])
+
+    src = rasterio.open('tmp/output.tif')
+    sampled_values = [x[0] for x in src.sample(coord_list)]
+
+    primary_featurelayer.gdf['guncrime_density'] = sampled_values
+
+    guncrime_classifier = mapclassify.Percentiles(primary_featurelayer.gdf['guncrime_density'], pct=[50, 75, 90, 95, 99, 100])
+    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].apply(guncrime_classifier)
+    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].astype(float)
+
+    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].replace([0, 1, 2, 3, 4, 5], ['Bottom 50%', 'Top 50%', 'Top 25%', 'Top 10%', 'Top 5%', 'Top 1%'])
+
+    return primary_featurelayer
diff --git a/src/data_utils/l_and_i.py b/src/data_utils/l_and_i.py
index 16389ab3..ca3c8bce 100644
--- a/src/data_utils/l_and_i.py
+++ b/src/data_utils/l_and_i.py
@@ -1,6 +1,5 @@
 from classes.featurelayer import FeatureLayer
 from constants.services import COMPLAINTS_SQL_QUERY, VIOLATIONS_SQL_QUERY
-import geopandas as gpd
 
 
 def l_and_i(primary_featurelayer):
@@ -20,9 +19,7 @@ def l_and_i(primary_featurelayer):
         .apply(lambda x: "; ".join([val for val in x if val is not None]))
         .reset_index()
     )
-    l_and_i_complaints.gdf = gpd.GeoDataFrame(
-        l_and_i_complaints.gdf, geometry="geometry"
-    )
+    l_and_i_complaints.rebuild_gdf()
 
     # collapse complaints_gdf by address and concatenate the violationcodetitle values into a list with a semicolon separator
     # l_and_i_complaints.gdf = l_and_i_complaints.gdf.groupby('address')['service_name'].apply(lambda x: '; '.join([val for val in x if val is not None])).reset_index()
@@ -80,9 +77,7 @@ def l_and_i(primary_featurelayer):
         .apply(lambda x: "; ".join([val for val in x if val is not None]))
         .reset_index()
     )
-    l_and_i_complaints.gdf = gpd.GeoDataFrame(
-        l_and_i_complaints.gdf, geometry="geometry"
-    )
+    l_and_i_complaints.rebuild_gdf()
 
     # rename the column to 'li_violations'
     l_and_i_violations.gdf.rename(
diff --git a/src/data_utils/rco_geoms.py b/src/data_utils/rco_geoms.py
new file mode 100644
index 00000000..f25c8a7a
--- /dev/null
+++ b/src/data_utils/rco_geoms.py
@@ -0,0 +1,18 @@
+from classes.featurelayer import FeatureLayer
+from constants.services import RCOS_LAYERS_TO_LOAD
+
+
+def rco_geoms(primary_featurelayer):
+    rco_geoms = FeatureLayer(
+        name="RCOs", esri_rest_urls=RCOS_LAYERS_TO_LOAD
+    )
+
+    rco_aggregate_cols = ['ORGANIZATION_NAME', 'ORGANIZATION_ADDRESS', 'PRIMARY_EMAIL', 'PRIMARY_PHONE']
+
+    rco_geoms.gdf["rco_info"] = rco_geoms.gdf[rco_aggregate_cols].apply(lambda x: "; ".join(map(str, x)), axis=1) + "; " + rco_geoms.gdf['geometry'].apply(str)
+    rco_geoms.gdf = rco_geoms.gdf[['rco_info', 'geometry']]
+    rco_geoms.rebuild_gdf()
+
+    primary_featurelayer.spatial_join(rco_geoms)
+
+    return primary_featurelayer
diff --git a/src/data_utils/tree_canopy.py b/src/data_utils/tree_canopy.py
new file mode 100644
index 00000000..e6b451e7
--- /dev/null
+++ b/src/data_utils/tree_canopy.py
@@ -0,0 +1,30 @@
+import requests
+import io
+import zipfile
+import geopandas as gpd
+from classes.featurelayer import FeatureLayer
+from config.config import USE_CRS
+
+def tree_canopy(primary_featurelayer):
+    tree_url = 'https://national-tes-data-share.s3.amazonaws.com/national_tes_share/pa.zip.zip'
+
+    tree_response = requests.get(tree_url)
+
+    with io.BytesIO(tree_response.content) as f:
+        with zipfile.ZipFile(f, 'r') as zip_ref:
+            zip_ref.extractall("tmp/")
+            
+    pa_trees = gpd.read_file("tmp/pa.shp")
+    pa_trees = pa_trees.to_crs(USE_CRS)
+    phl_trees = pa_trees[pa_trees['county'] == 'Philadelphia County']
+    phl_trees = phl_trees[['tc_gap', 'geometry']]
+
+    phl_trees.rename(columns={'tc_gap': 'tree_canopy_gap'}, inplace=True)
+
+
+    tree_canopy = FeatureLayer('Tree Canopy')
+    tree_canopy.gdf = phl_trees
+    
+    primary_featurelayer.spatial_join(tree_canopy)
+
+    return primary_featurelayer
diff --git a/src/script.py b/src/script.py
index 15a43794..ae879cde 100644
--- a/src/script.py
+++ b/src/script.py
@@ -4,6 +4,9 @@
 from data_utils.city_owned_properties import city_owned_properties
 from data_utils.phs_properties import phs_properties
 from data_utils.l_and_i import l_and_i
+from data_utils.rco_geoms import rco_geoms
+from data_utils.tree_canopy import tree_canopy
+from data_utils.gun_crimes import gun_crimes
 
 
 """
@@ -33,10 +36,27 @@
 """
 vacant_properties = l_and_i(vacant_properties)
 
+"""
+Merge RCOs
+"""
+vacant_properties = rco_geoms(vacant_properties)
+
+
+"""
+Load Tree Canopy Data
+"""
+vacant_properties = tree_canopy(vacant_properties)
+
+
+"""
+Gun Crime Data
+"""
+vacant_properties = gun_crimes(vacant_properties)
+
+
+
+# Clean up
 vacant_properties.gdf.to_postgis(
     "vacant_properties_end", conn, if_exists="replace", index=False
 )
-
-# Clean up
-
 conn.close()

From 68adcd01fe1ec052e14570fe949e42e14d327fb1 Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Sun, 27 Aug 2023 20:21:59 -0400
Subject: [PATCH 6/7] formatting

---
 src/classes/featurelayer.py   |  4 +--
 src/data_utils/gun_crimes.py  | 68 +++++++++++++++++++++++------------
 src/data_utils/rco_geoms.py   | 21 +++++++----
 src/data_utils/tree_canopy.py | 20 ++++++-----
 src/script.py                 |  1 -
 5 files changed, 74 insertions(+), 40 deletions(-)

diff --git a/src/classes/featurelayer.py b/src/classes/featurelayer.py
index fb87d6d9..7a2dbbf2 100644
--- a/src/classes/featurelayer.py
+++ b/src/classes/featurelayer.py
@@ -54,7 +54,7 @@ def __init__(
                 if not psql_exists:
                     self.load_data()
         else:
-            print('Initialized FeatureLayer with no data.')
+            print("Initialized FeatureLayer with no data.")
 
     def check_psql(self):
         try:
@@ -156,4 +156,4 @@ def spatial_join(self, other_layer, how="left", predicate="intersects"):
         self.gdf.drop_duplicates(inplace=True)
 
     def rebuild_gdf(self):
-        self.gdf = gpd.GeoDataFrame(self.gdf, geometry="geometry")
\ No newline at end of file
+        self.gdf = gpd.GeoDataFrame(self.gdf, geometry="geometry")
diff --git a/src/data_utils/gun_crimes.py b/src/data_utils/gun_crimes.py
index 4fb2ffcc..62e816ab 100644
--- a/src/data_utils/gun_crimes.py
+++ b/src/data_utils/gun_crimes.py
@@ -8,6 +8,7 @@
 from rasterio.transform import Affine
 import mapclassify
 
+
 def gun_crimes(primary_featurelayer):
     # Initialize gun_crimes object
     gun_crimes = FeatureLayer(name="Gun Crimes", carto_sql_queries=GUNCRIME_SQL_QUERY)
@@ -25,7 +26,9 @@ def gun_crimes(primary_featurelayer):
     X = np.array(list(zip(x, y)))
 
     # Generate grid for plotting
-    x_grid, y_grid = np.linspace(x.min(), x.max(), 1000), np.linspace(y.min(), y.max(), 1000)
+    x_grid, y_grid = np.linspace(x.min(), x.max(), 1000), np.linspace(
+        y.min(), y.max(), 1000
+    )
     xx, yy = np.meshgrid(x_grid, y_grid)
     grid_points = np.array([xx.ravel(), yy.ravel()]).T
 
@@ -43,38 +46,59 @@ def gun_crimes(primary_featurelayer):
 
     # Save the plot in tmp folder
     plt.pcolormesh(xx, yy, zz)
-    plt.scatter(x, y, c='red', s=0.005)
+    plt.scatter(x, y, c="red", s=0.005)
     plt.colorbar()
-    plt.savefig('tmp/kde.png')
-
+    plt.savefig("tmp/kde.png")
 
     # Define the affine transform
     transform = Affine.translation(min_x, min_y) * Affine.scale(x_res, y_res)
 
     # Export as raster
-    with rasterio.open('tmp/output.tif', 'w', driver='GTiff',
-                    height=zz.shape[0],
-                    width=zz.shape[1],
-                    count=1, dtype=zz.dtype,
-                    crs=USE_CRS,
-                    transform=transform) as dst:
+    with rasterio.open(
+        "tmp/output.tif",
+        "w",
+        driver="GTiff",
+        height=zz.shape[0],
+        width=zz.shape[1],
+        count=1,
+        dtype=zz.dtype,
+        crs=USE_CRS,
+        transform=transform,
+    ) as dst:
         dst.write(zz, 1)
-    
-    primary_featurelayer.gdf['centroid'] = primary_featurelayer.gdf.geometry.centroid
-    
-    coord_list = [(x,y) for x,y in zip(primary_featurelayer.gdf['centroid'].x, primary_featurelayer.gdf['centroid'].y)]
 
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=['centroid'])
+    primary_featurelayer.gdf["centroid"] = primary_featurelayer.gdf.geometry.centroid
 
-    src = rasterio.open('tmp/output.tif')
-    sampled_values = [x[0] for x in src.sample(coord_list)]
+    coord_list = [
+        (x, y)
+        for x, y in zip(
+            primary_featurelayer.gdf["centroid"].x,
+            primary_featurelayer.gdf["centroid"].y,
+        )
+    ]
 
-    primary_featurelayer.gdf['guncrime_density'] = sampled_values
+    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=["centroid"])
 
-    guncrime_classifier = mapclassify.Percentiles(primary_featurelayer.gdf['guncrime_density'], pct=[50, 75, 90, 95, 99, 100])
-    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].apply(guncrime_classifier)
-    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].astype(float)
+    src = rasterio.open("tmp/output.tif")
+    sampled_values = [x[0] for x in src.sample(coord_list)]
 
-    primary_featurelayer.gdf['guncrime_density'] = primary_featurelayer.gdf['guncrime_density'].replace([0, 1, 2, 3, 4, 5], ['Bottom 50%', 'Top 50%', 'Top 25%', 'Top 10%', 'Top 5%', 'Top 1%'])
+    primary_featurelayer.gdf["guncrime_density"] = sampled_values
+
+    guncrime_classifier = mapclassify.Percentiles(
+        primary_featurelayer.gdf["guncrime_density"], pct=[50, 75, 90, 95, 99, 100]
+    )
+    primary_featurelayer.gdf["guncrime_density"] = primary_featurelayer.gdf[
+        "guncrime_density"
+    ].apply(guncrime_classifier)
+    primary_featurelayer.gdf["guncrime_density"] = primary_featurelayer.gdf[
+        "guncrime_density"
+    ].astype(float)
+
+    primary_featurelayer.gdf["guncrime_density"] = primary_featurelayer.gdf[
+        "guncrime_density"
+    ].replace(
+        [0, 1, 2, 3, 4, 5],
+        ["Bottom 50%", "Top 50%", "Top 25%", "Top 10%", "Top 5%", "Top 1%"],
+    )
 
     return primary_featurelayer
diff --git a/src/data_utils/rco_geoms.py b/src/data_utils/rco_geoms.py
index f25c8a7a..a508358e 100644
--- a/src/data_utils/rco_geoms.py
+++ b/src/data_utils/rco_geoms.py
@@ -3,14 +3,23 @@
 
 
 def rco_geoms(primary_featurelayer):
-    rco_geoms = FeatureLayer(
-        name="RCOs", esri_rest_urls=RCOS_LAYERS_TO_LOAD
-    )
+    rco_geoms = FeatureLayer(name="RCOs", esri_rest_urls=RCOS_LAYERS_TO_LOAD)
 
-    rco_aggregate_cols = ['ORGANIZATION_NAME', 'ORGANIZATION_ADDRESS', 'PRIMARY_EMAIL', 'PRIMARY_PHONE']
+    rco_aggregate_cols = [
+        "ORGANIZATION_NAME",
+        "ORGANIZATION_ADDRESS",
+        "PRIMARY_EMAIL",
+        "PRIMARY_PHONE",
+    ]
 
-    rco_geoms.gdf["rco_info"] = rco_geoms.gdf[rco_aggregate_cols].apply(lambda x: "; ".join(map(str, x)), axis=1) + "; " + rco_geoms.gdf['geometry'].apply(str)
-    rco_geoms.gdf = rco_geoms.gdf[['rco_info', 'geometry']]
+    rco_geoms.gdf["rco_info"] = (
+        rco_geoms.gdf[rco_aggregate_cols].apply(
+            lambda x: "; ".join(map(str, x)), axis=1
+        )
+        + "; "
+        + rco_geoms.gdf["geometry"].apply(str)
+    )
+    rco_geoms.gdf = rco_geoms.gdf[["rco_info", "geometry"]]
     rco_geoms.rebuild_gdf()
 
     primary_featurelayer.spatial_join(rco_geoms)
diff --git a/src/data_utils/tree_canopy.py b/src/data_utils/tree_canopy.py
index e6b451e7..bc133893 100644
--- a/src/data_utils/tree_canopy.py
+++ b/src/data_utils/tree_canopy.py
@@ -5,26 +5,28 @@
 from classes.featurelayer import FeatureLayer
 from config.config import USE_CRS
 
+
 def tree_canopy(primary_featurelayer):
-    tree_url = 'https://national-tes-data-share.s3.amazonaws.com/national_tes_share/pa.zip.zip'
+    tree_url = (
+        "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/pa.zip.zip"
+    )
 
     tree_response = requests.get(tree_url)
 
     with io.BytesIO(tree_response.content) as f:
-        with zipfile.ZipFile(f, 'r') as zip_ref:
+        with zipfile.ZipFile(f, "r") as zip_ref:
             zip_ref.extractall("tmp/")
-            
+
     pa_trees = gpd.read_file("tmp/pa.shp")
     pa_trees = pa_trees.to_crs(USE_CRS)
-    phl_trees = pa_trees[pa_trees['county'] == 'Philadelphia County']
-    phl_trees = phl_trees[['tc_gap', 'geometry']]
+    phl_trees = pa_trees[pa_trees["county"] == "Philadelphia County"]
+    phl_trees = phl_trees[["tc_gap", "geometry"]]
 
-    phl_trees.rename(columns={'tc_gap': 'tree_canopy_gap'}, inplace=True)
+    phl_trees.rename(columns={"tc_gap": "tree_canopy_gap"}, inplace=True)
 
-
-    tree_canopy = FeatureLayer('Tree Canopy')
+    tree_canopy = FeatureLayer("Tree Canopy")
     tree_canopy.gdf = phl_trees
-    
+
     primary_featurelayer.spatial_join(tree_canopy)
 
     return primary_featurelayer
diff --git a/src/script.py b/src/script.py
index ae879cde..e1608111 100644
--- a/src/script.py
+++ b/src/script.py
@@ -54,7 +54,6 @@
 vacant_properties = gun_crimes(vacant_properties)
 
 
-
 # Clean up
 vacant_properties.gdf.to_postgis(
     "vacant_properties_end", conn, if_exists="replace", index=False

From 3da0b7b623df9d317b048c3f44ecfa42c4cee35b Mon Sep 17 00:00:00 2001
From: Brandon Cohen <brandon.f.cohen@gmail.com>
Date: Sun, 27 Aug 2023 21:15:35 -0400
Subject: [PATCH 7/7] update gitignore

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 05e29933..575eaeda 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 /notebooks/.ipynb_checkpoints/
 .idea
-src/tmp
\ No newline at end of file
+src/tmp
+.DS_Store
\ No newline at end of file