Coursework from Principles of Urban Informatics class (Fall 2017)
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Updated
Dec 20, 2017 - Jupyter Notebook
Coursework from Principles of Urban Informatics class (Fall 2017)
London Tree Explorer R Shiny app
LocXplore: A system for profiling regions in Berlin
UrbanFACET: Visually Profiling Cities from Mobile Device Recorded Movement Data of Millions of City Residents
Geospatial Analysis project of St. Louis 2018
Measuring traffic congestion and noise pollution with computer vision and noise sensors
Urban Growth Modeling Toolkit
A TerraView plugin to compute global and local indices of spatial segregation among population groups.
The repository contains diverse economic and socio-demographic data or Rome retrieved through APIs and scraping.
Data Analysis of Energy Trends The main objective of this analysis is to identify renewable energy key performance indicators (KPIs) and trends for the reporting CDP Regions.
Reproducible Python notebooks: Using satellite imagery to analyse dynamics of urban neighbourhood.
Personal Website: ashleylouie.com
Systematic approaches to collecting, analyzing, modeling, and interpreting data to inform research, and, ultimately, urban planning practice and policymaking.
This preliminary study links spatial data science, street network analysis and urban planning with San Francisco as a testing ground.
The app explores the business landscape in the borough of Coraopolis and untangles the revenues by industry and the relation between rent, square footage, and the location.
Packaged repository for the final studio project for the Artificial Intelligence seminar at the MACAD Masters
Code to build the website tutorials for the Social Life of Neighborhoods course offered through Stanford's Sociology department in the spring quarter of 2021.
Jupyter notebooks for the workshop *Introduction to Python language and Google Colab for spatial data visualization and analysis*
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