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<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>ABOUT</title>
<script src="jquery.min.js"></script>
<!-- <script src="d3.tip.js"></script> -->
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<body>
<div class = "container-">
<div id = "header">
<div id = "title"><a href="index.html"><span style="color:#fff">ATLAS of LIGHTING</span> </a><sup><small><i>beta</i></small></sup></div>
<div id = "moreinfo">
<a href ="index.html">HOME</a>
</div>
<div id = "logos"><a href="https://www.phillips.com/" target="_blank"><img src="logos/philips_white.png" style="width:80px"></a> <a href="http://www.civicdatadesignlab.org/" target="_blank"><img src="logos/cddl_white.png" style="width:80px "></a><a href="http://cau.mit.edu/" target="_blank"><img src="logos/cau.jpg" style="height:30px "></a></div>
</div>
<div id = "about-1">
<p class = "section-title">About<p>
<p class = "subsection-title">MSA Selection</p>
<p>Today’s digital revolution has revolutionized the way we process the volume of information that cities produce every day. Especially with the emergence of digital data and ubiquitous use of mobile social network services, new opportunities arise for researchers to develop tools grounded in new data sources. What makes these sources particularly useful for geospatial analysis is that they are available across municipal, provincial, and national borders. Wherever the web service has users, data is available which means that researchers are no longer dependent on conventional data sources.</p>
<p>Atlas of Lighting is a joint project between Philips, MIT Center for Advanced Urbanism and MIT Civic Data Design Lab that responds to the need for for an integrated and scientific understanding of the dynamics of metropolitan areas around the world. It is an interactive mapping tool in which the user can combine, isolate or cross-reference both quantitative and qualitative datasets to get a clear view of how cities operate.</p>
<p>The tool has a potential to create the first comprehensive city dashboard anywhere that digital data exist. With the inclusion of additional urban areas, the atlas aims to make comparative analysis of metro areas which can will be crucial for policy makers to make integrated and science based decisions.</p>
<p class = "section-title">Team</p>
<p>Alan Berger | Co-Director, <a href="http://cau.mit.edu/" target="_blank">Center for Advanced Urbanism</a> </p>
<p>Sarah Williams | Director, <a href="http://www.civicdatadesignlab.org/" target="_blank">Civic Data Design Lab </a> </p>
</br>
<p>Hayrettin Gunc | Project Manager</p>
<p>Armin Akhavan | Lead Data Analyst and developer</p>
<p>Andres Achury Garcia | Data Analyst</p>
<p>Dennis Cameron Harvey | Data Analyst</p>
<p>Sera Tolgay | Data Analyst</p>
<p>Rida Qadri | Data Analyst</p>
<p>Jia Zhang | Website and Visualization</p>
<p>
Special Thanks to:
Alexandru Darie
Rohit Kumar
</div>
<div id = "about-2">
<p class = "section-title">Data<p>
<p class = "subsection-title">Population Density & Household Median Income </p>
<a href="http://www.census.gov/programs-surveys/acs/" target="_blank">http://www.census.gov/programs-surveys/acs/</a>
<p>The American Community Survey (ACS) is conducted by the US Census and sent to a portion of the population every year. As of the 2014 (most recent) ACS release there are 1 year and 5 year estimates. For the tool, 2014 ACS - 5 Year Estimate surveys are used because of the large geographic boundaries (MSA) of the focus areas. Unlike 1 year estimates, 5 year estimates use largest sample size which makes it most reliable for analyzing very small populations.</p>
<p class = "subsection-title">Development Intensity</p>
<a href="http://www.mrlc.gov/nlcd11_data.php" target="_blank">http://www.mrlc.gov/nlcd11_data.php</a>
<p>National Land Cover Database 2011 (NLCD 2011) of USGS Land Cover Institute is “a 16-class (additional four classes in Alaska only) land cover classification scheme that has been applied consistently across all 50 United States and Puerto Rico at a spatial resolution of 30 meters." Since the research used this dataset as a proxy for intensity of development, three layers were isolated: low intensity developed, medium intensity developed and high intensity developed.</p>
<p class = "subsection-title">Night Lights</p>
<a href="http://ngdc.noaa.gov/eog/viirs/download_monthly.html" target="_blank">http://ngdc.noaa.gov/eog/viirs/download_monthly.html</a>
<p>VIIRS Nighttime Lights dataset is maintained by the Earth Observation Group at NOAA National Geophysical Data Center. The dataset is used to measure quantity of light from earths surface on a radiometric sensor. The Day Night Band monthly composites average all cloud free images from that month. In order to avoid errors due to seasonal changes, cloud free composites of every month in 2015 were downloaded and then averaged using GIS software.</p>
<p class = "subsection-title">Place Density</p>
<a href="https://developers.google.com/places/" target="_blank">https://developers.google.com/places/</a>
<p>The Places dataset belongs to Google Places API which is a collection of georeferenced businesses, institutions, parks, and other points of interest. This dataset is most commonly used as part of the Google maps services. For the research, captured places were binned the points into the cells of a grid that covers the entire MSA. GIS software were used to calculate the the number Google Places in each grid cell.</p>
</div>
<div id = "about-3">
<p class = "section-title">Selection Factors and Methodological Process<p>
<p>In order to select geographically diverse and economically vibrant set of U.S. cities to best represent the variation of urbanized areas in the country, the team conducted a selection process. Typically, research at this scale focuses on the top 100 MSAs (Metropolitan Statistical Area) by population. This convenient metric accounts for a large portion of the nation’s populous and economy. The research uses one primary cutoff (population) and several factors (pop. growth, pop.-weighted density, and Gross Metropolitan Product) to sort the list of potential MSAs for further study. The rationale and steps in the process are detailed below:</p>
<p>- Population Threshold<br/> Limiting by population is probably the most common feature of research attempting to compare metros statistically. The logic behind segregating the metros into size classes based on population recognizes both the need to compare apples to apples and that scale thresholds exist that impact urban infrastructures, physical form and economic developments.
<p>- Population Growth<br/> The selection process requires a factor that can transparently distinguish between cities that grew historically and those that grow more rapidly in recent times.Thus, we utilize recent population growth (2000-2014) as a factor to be certain that we capture the newest growth rather than the biggest historical growth. The diversity signifies drastically different approaches to infrastructure creation and usage for these urbanized areas and each should be represented in the final selection set</p>
<p>- Gross Metropolitan Product<br/> Another critical dimension when attempting to capture the diversity of metropolitan performance is economic production.Gross Metropolitan Product from the Bureau of Economic Analysis captures the production of the industry sectors in each metro area.The data used in this analysis are in real, chain-weighted dollars (i.e., inflation-adjusted). For this project, the straightforward use of real GMP of all industry sectors is used to avoid unduly biasing the selection process on underdeveloped regional economic theory.</p>
<p>- Population Density<br/> Population density is the final factor that will help ensure a diverse study set of MSAs. Density varies widely in U.S. urban areas, ranging from the peaks of downtown Manhattan to the suburban districts of Dallas and even sparse rural areas in the outlying counties of metropolitan areas. Thus, varying urban densities may invoke different amounts and configurations of lighting infrastructure. The selected metros need to display the full range of U.S. densities to properly assess the potential lighting infrastructure/economic variable connections.</p>
<p>With these precedents in mind, the plot helps reveal a system of MSAs that separate into at least five identifiable groups. The following 5 selection sets will serve as the primary source of the final selection to be determined by data compatibility/availability (see plot figure for full groups). The sets were determined by selecting the highest, lowest, and the median 2010 population-weighted density MSA from each group (listed in that order below). The MSA with a density next closest to the median was chosen as the alternate.</p>
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