/gi4dm-2022-paper

Conference paper for Gi4DM and Urban Geo-Informatics 2022

Primary LanguageJupyter Notebook

Generative Design for Precision Geo-Interventions

Richard Wen, Songnian Li
rrwen.dev@gmail.com, snli@ryerson.ca

Abstract

The purpose of this research is to develop an approach for a Spatial Decision Support System (SDSS) that integrates Geographic Information Systems (GIS), Automated Machine Learning (AutoML), and Hyperparameter Optimization (HPO) to generate precision geo-interventions based on standardized geospatial data and user design constraints. The geo-intervention generation approach involves three steps: (1) Geo-binning, (2) AutoML, and (3) Prediction Optimization. Geo-binning is used to standardize geospatial data into regularized grids as inputs into AutoML models. Prediction optimization generates geo-interventions by applying user-design constraints and optimizing AutoML model output to find optimized input variables that form precise geo-interventions. An experiment in reducing road traffic collisions using infrastructural changes in Toronto, Ontario, Canada was done to evaluate the geo-intervention generation approach. The results of the experiment found that changing the number of schools, red light cameras, and transit shelters in high traffic areas could potentially halve the total number of traffic collisions according to a 80 by 80 geo-binned grid Auto-Sklearn model with a Mean Absolute Error (MAE) of 117.68. It was also found that user design constraints heavily affected the prediction optimization step as when the areas were altered to an alternative grid of cells with scarce infrastructure, the number of predicted collisions rose by 6127 collisions. Thus, limitations of this study included subjectivity in user design constraints, scalability, and interactivity. Future work involves improving modelling/optimization efficiency and developing an interactive interface for exploring generated precision geo-interventions.

History

Date Note
Wednesday, November 2, 2022 8:30-10am (Beijing TZ) Presented slides (12 min, 3 min questions) for Urban Geo-Info Session (Link)
Thursday, October 27, 2022 Full paper published to ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (PDF | Web)
Tuesday, October 23, 2022 Presentation slides submitted to Urban Geo-Information track for November 2, 2022 Urban Geo-Info Session at 8:30-10am (PDF | PPT)
Tuesday, September 20, 2022 Full paper submitted to the Urban Geo-Information track (PDF | DOC)
Thursday, September 1, 2022 Abstract accepted to the Urban Geo-Inforomation track (PDF | DOC)
Thursday, August 18, 2022 Abstract submitted to the Urban Geo-Information track (PDF | DOC)

Install

In Windows:

  1. Install Anaconda3
  2. Install Windows Subsystem wsl --install
  3. Install dos2unix wsl sudo apt install dos2unix
    • Note: You may be promoted for a password due to sudo privileges
  4. Run bin/setup.bat to convert scripts in bin folder for windows use
  5. Enter a Windows Subsystem terminal with wsl
  6. Run bin/install to create a conda environment
  7. Activate the conda environment (named gi4dm-2022-paper)
wsl --install
wsl sudo apt install dos2unix
bin\setup
wsl
source bin/install.sh
source bin/activate.sh

In Linux/Mac OS:

  1. Install Anaconda3
  2. Run bin/install to create a conda environment
  3. Activate the conda environment (named gi4dm-2022-paper)
source bin/install.sh
source bin/activate.sh

References

Contact

Richard Wen rwen@ryerson.ca and Songnian Li snli@ryerson.ca