This is the GitHub repository corresponding to the algorithm Swarm-based spaatial memetic algorithm (SPATIAL). Here, we apply SPATIAL to solve the problem of school boundary formation (also called school redistricting).
The code is written in Python3.6 and the experiments were run on a machine using Ubuntu 18.04 LTS. You can follow the commands below for setting up your project.
Assuming you have Python3, set up a virtual environment
pip install virtualenv
virtualenv -p python3 venv
Always make sure to activate it before running any python script of this project
source venv/bin/activate
Install the required packages contained in the file requirements.txt. This is a one-time thing, make sure the virtual environment is activated before performing this step.
pip install -r requirements.txt
Note that some geospatial packages in Python require dependencies like GDAL to be already installed.
cd ./src
You can simulate all the experiments using the following command:
make SPATIAL
OR
Simulate experiments for
- Elementary school
./run_algo.py -s ES
- Middle school
./run_algo.py -s MS
- High school
./run_algo.py -s HS
Deactivate it before exiting the project
deactivate
The geospatial data used here is of LCPS school district for the school year 2019-20. The data has been pre-processed for usage and may not accurately represent the policies of LCPS.
If you use this data/code for your work, please consider citing the paper:
@inproceedings{10.1145/3397536.3422265,
author = {Biswas, Subhodip and Chen, Fanglan and Chen, Zhiqian and Lu, Chang-Tien and Ramakrishnan, Naren},
title = {Incorporating Domain Knowledge into Memetic Algorithms for Solving Spatial Optimization Problems},
year = {2020},
isbn = {9781450380195},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3397536.3422265},
doi = {10.1145/3397536.3422265},
booktitle = {Proceedings of the 28th International Conference on Advances in Geographic Information Systems},
pages = {25–35},
numpages = {11},
keywords = {Metaheuristic, domain knowledge, spatial optimization},
location = {Seattle, WA, USA},
series = {SIGSPATIAL '20}
}
Should you have queries, feel free to send an email to subhodip [at] cs.vt.edu