Description
Overview
Investigate whether there are meaningful trends associated with metro stops and metro lines with regards to requests tracked by 311-data in LA County.
Action Items
- Define requirements for 311 data (adding notes to the resources section and discussing at the Data Science CoP meeting
- Do you need a one-time or ongoing dump of the data?
- Do you need subset of data (i.e. certain years) or the entire data set (approx. 4 million rows or 11 GB)?
- If a subset is needed, please define subset characteristics (i.e. date range, etc.)
- Do you need online access via an API or a download of data?
- Add dependency label and put in the icebox until 311 data is provided
- Find available data sources and add to Resources section below
- Determine is this is one-time or ongoing project (and assign appropriate label)
- Write one-sheet
- Define stakeholder
- Summarize project including value add
- Define project 6 month roadmap
- Detail history (if any)
- Define tools to be used to visualize combined data
- Create issues for the following
- EDA (Exploratory Data Analysis) of metro data
- Identify correlations between distance from metro stop and request type
- Determine if correlations observed are solely due to metro stop or are more broadly associated with population density or other factors
- Combine geolocation data for metro lines with district types
- Compare correlations/trends between different districts within each type
- Compare LA county data with other California counties, compare with district types within county. (Post MVP)
- Compare with statewide trends and within district types. (Post MVP)
Resources
Information about 311 Data here
Access 311 data here
http://geohub.lacity.org/datasets/metro-rail-lines-stops
https://developer.metro.net/docs/gis-data/overview/
District types issue: #118
use 2019 data for 311
streetlights
crime
metrostops
tools
google colab, sklearn, pandas
Work in progress
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Type
Projects
Status
Prioritized Backlog
Status
Currently Recruiting