Skip to content

This repository is a tool that acts as a plug-in for QGIS. By using the tool, you can easily visualize statistical data such as human flow data on a map.

License

Notifications You must be signed in to change notification settings

kaitoyoshida/people_flow_visualization

 
 

Repository files navigation

人流データ可視化ツール / MLIT People Flow Visualization Tool

「人流データ可視化ツール」(以下、本ツール)は国土交通省不動産・建設経済局情報推進課にて実施した 令和4年度人流データの可視化等検討調査業務 において試作開発したものです。 本ツールは人流データの利活用促進のために、これまで GISや BIツール等で人流データを取り扱ったことのないユーザーに向けて、人流データの活用に取り組むきっかけとなることを狙って試作開発したものです。このため、本ツールは人流データの可視化においてよく使われる表現に絞ってツールをパッケージ化し、できるだけ簡易に可視化できるようにしております。


The "MLIT People Flow Visualization Tool" was developed as a prototype in the 2022 People Flow Data Visualization Study conducted by the Information Promotion Division, Real Estate and Construction Economy Bureau, Ministry of Land, Infrastructure, Transport and Tourism.

This tool was developed as a prototype to promote the utilization of people flow data, aiming to provide an opportunity for users who have never handled people flow data with GIS or BI tools to start utilizing people flow data.

For this reason, we have packaged this tool by focusing on expressions that are often used in visualizing people flow data to make it as easy as possible to visualize.

サンプル画像/ Sample images

動作環境/ System requirement

本ツールはQGIS 3.28にて動作確認をしております。


This tool has been tested on QGIS 3.28.

利用方法/ How to use

利用方法については、使い方をご確認ください。


Please refer to how to use for details on how to use the tool.

利用ライブラリ等/ Use libraries


ライセンス/ Licence

本ツールは GNU GENERAL PUBLIC LICENSE v2 ライセンスが設定されています。 GNU GENERAL PUBLIC LICENSE Version 2, June 1991


This tool is licensed under the GNU GENERAL PUBLIC LICENSE v2 license. GNU GENERAL PUBLIC LICENSE Version 2, June 1991

About

This repository is a tool that acts as a plug-in for QGIS. By using the tool, you can easily visualize statistical data such as human flow data on a map.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 50.4%
  • HTML 49.6%