This is the City Mobility Equity Metric Dashboard

Click here to see the website.

The following describes project structure, CSV files store calculated results (MEM & MI) under table folder:

• MobDashboard
  • data
    • transport flow and O/D matrix
  • Metric_data
    • MEM for transport network
  • www
    • introduction page pictures
  • bs_memresult1k.csv: Boston MEM results by different combinations
  • bs_merged_geoid_comm_data.csv: Boston geographic layer at neighborhood level and census data
  • bs_miresults.csv: contains MI in Boston (percentages of different service types refer to the weights toward MI)
  • dc_10.csv: contains Washington DC geometry and census data
  • flow_combine.csv: routes in Boston transport network, geometry data, from osrm
  • dc_memresult1k.csv: DC MEM combinations
  • dc_miresults.csv: DC mobility index
  • styles.css: shiny ui.

you can find calculated results for Los Angeles and New York City in similar file name. We use Python to calculate them.

MEMcode_sample.zip contains sample codes to calculate MI and MEM, and draw a map in DC. Please unzip "isochrone" folder before run jupyter notebook, as it is large.

The required libraries are below

want <- c("ggplot2", "readxl", "shiny", "igraph", "geojsonR", "shinycssloaders",
          "sf", "leaflet", "plotly", "dplyr", "shinyjs", "rintrojs")
need <- want[!(want %in% installed.packages()[,"Package"])]
if (length(need)) install.packages(need)
lapply(want, function(i) require(i, character.only=TRUE))
rm(want, need)

This dashboard is mainly based on work from Routing in Mixed Transportation Systems for Mobility Equity， A Mobility Equity Metric for Multi-Modal Intelligent Transportation Systems & On Mobility Equity and the Promise of Emerging Transportation Systems.