Create datasets to upload and publish data. Further organize your data using folders and assign metadata at both the file and dataset level.
Final memorandum summarizing the project's findings, model results, and recommendations for DART, written for a transit-agency stakeholder audience, with a technical appendix.
Python and Weka command-line pipeline that builds the accessibility_score target, selects features, and compares regression and clustering models for the Project Methodology assignment.
Correlation matrix, PCA, clustering, and model-comparison figures including real Weka Explorer and Experimenter screenshots supporting the Project Methodology write-up.
Feature-selected regression and clustering tables, the accessibility_score target variable, and raw Weka output from the Project Methodology assignment.
Python pipeline used to fetch, clip, and aggregate all raw data sources into the final dataset.
Python, Weka Visualize and Preprocess screenshots supporting the outlier, correlation, balance, spatial-trend, and equity findings in the write-up.
The 46 DART station walksheds, the engineered feature table, the Census overlay, and the final combined flat file used for modeling.
GTFS transit feed, OpenStreetMap extracts (crosswalks, trees, benches, sidewalks, shops, resources), Dallas zoning polygons, and Census block group/tract data before spatial aggregation.
This shapefile is a difference layer of the 2023 and 2018 data. This shapefile shows how infrastructure has changed from 2018 to 2023 and hos infrastrcture desert hexagons have transitioned.
This shapefile shows which infrastructure is deficient and number of infrastructure deficiencies in each hexagon.
This shapefile shows which infrastructure is deficient and number of infrastructure deficiencies in each hexagon.
This is our data for lithium ion battery, all experimental datasets are created under our lab ASPEN lab at SMU.
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