Term project materials from CEE 5-7303 Citizen Engineering Using Machine Learning
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This dataset contains info from the NCTCOG Transit Accessibility Tool and the 2024 Bond Infrastructure Priority Grid.
This dataset points are all City of Dallas located.
The attributes in this set of data are:
% Population 65 and over
% Population 14 and under
% Population below poverty
% Population with disability
% Veteran population
% No car population
% Total minority population
% Hispanic population
% Black population
% American Indian population
% Asian population
% Hawaiian Pacific Islander population
% Other population
% 2 or more races population
% Total limited English proficiency population
% Spanish English proficiency population
% IE English proficiency population
% Asian English proficiency population
% Other English proficiency population
Total Overlay Score (bond allocation priority score - up to 16)
This is the PDF of the final project memo for the in-person grad teams project on healthcare infrastructure in Dallas, TX.
This Data set includes all excel (.csv) files that the in-person grad team used to investigate existing healthcare infrastructure in Dallas, TX.
Attributes: Clinics (name and location), Hospitals (name and location), Urgent Cares (name and location), census block data (population, size, and location)
Credit: the Clinics, Hospitals, and Urgent Cares are from John's database; the Census data is from the US Census
Note: the flat files for Clowder are called Copy of Spatialjoin, with Spatialjoin being an earlier version
This file is our group's final presentation.
School Enrollment from Three different boundaries (Victory Meadows, Bishop Arts, and Deep Ellum) From 2010-2020.
Occupancy Percentage of occupied houses from Three different boundaries (Victory Meadows, Bishop Arts, and Deep Ellum) From 2010-2020.
Employment Percentage from Three different boundaries (Victory Meadows, Bishop Arts, and Deep Ellum) From 2010-2020.
This dataset is not a real dataset, just a test.
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