What is See-Click-Fix?
See-Click-Fix is a fancy new app that assists residents in communicating with their local governments. Users report issues that they think need to be addressed by local agencies through the app, and the reports get sent directly to the municipal 3-1-1 service center. The app is used as an addition to 3-1-1 calls, and aims to lower the barriers residents face when reporting issues to city governments.
See-Click-Fix and 3-1-1 data in Oakland (2012-present) are available online. The mapped See-Click-Fix data include reports categorized as issue relating to transportation engineering or street infrastructure (categorized by Oakland’s 3-1-1 department) from 2013-2016 (n = 23,316). The dataset was downloaded, geocoded using ArcGIS then uploaded into Carto db.
What are Communities of Concern?
Communities of Concern is a categorization created by the Bay Area’s Metropolitan Transportation Commission (MTC) that categorizes census tracts in the Bay Area as Communities of Concern based on the following demographic variables (collected through ACS 2010-2014):
- Racial Minority (70% threshold)
- Low-Income (less than 200% of Fed. poverty level, 30% threshold)
- Level of Eng. Prof. (20% threshold)
- Elderly (10% threshold)
- Zero-Vehicle Households (10% threshold)
- Single Parent Households (20% threshold)
- Disabled (25% threshold), Rent-Burdened Households (15% threshold).
If a census tract exceeds both threshold values for Low-Income and Racial Minority shares of the population or exceeds the threshold value for Low-Income and also exceeds the threshold values for three or more variables, the tract is defined as a COC.
The map above shows depicts the average household annual incomes in Oakland’s Communities of Concern based on ACS 2010-2014 data.
Communities of Concern data was accessed through MTC’s ArcGIS website.
What are these maps saying?
See-Click-Fix is supposed to remove the barriers for residents to report issues to their local government, however in order to report an issue, a user must have a Smart Phone with the See-Click-Fix app downloaded and either data coverage or an internet connection. In neighborhoods with high proportions of households who don’t have the income to afford Smart Phones, data coverage or internet, one would expect there to be fewer See-Click-Fix reports.
These maps are useful for understanding where See-Click-Fix reports are being made in Oakland compared to where low income households are located. The first map shows the number of See-Click-Fix reports made in each census tract per capita, and Communities of Concern outlined in red. The second map shows average household annual income in Communities of Concern in Oakland. In comparing the two maps, we can begin to analyze whether their is a geographic correlation between income levels and See-Click-Fix reports. However, further data analysis needs to be done before any conclusions about these correlations can be made.
How were these maps made?
I have been working with the See-Click-Fix (SCF) data for the last few months in ArcGIS, so I was very excited try working with the data in Carto. I first tried to upload a csv file that included x, y coordinates and addresses to Carto, however I soon realized that the data set was too large for Carto to geocode the addresses, and I couldn’t find a way to georeference the data using x, y coordinates.
Instead, I uploaded shp files of the SCF and Communities of Concern (CoC) data to Carto. This was much easier, however I do want to find a way to geocode addresses and georeference x, y coordinates at some point.
I then clipped CoC data to a shp file of the Oakland city boundaries. I used the analysis tools to join the SCF points to a census tract layer, then calculated the per capita reports in each census tract.
I chose to show SCF per capita and income in four and five buckets to allow for an easier comparison between the two maps. I originally wanted to divide the data into four buckets for both maps, however I’m unsure how to determine the math behind determining the size of the buckets, which meant dividing the income data into four categories didn’t work in terms of a visualization. It would be useful to know how the categorization works in Carto.
I also found it frustrating trying to edit the imbedded link (my two map are at different scales in the imbedded link, however in the Carto dashboard, the maps are made at the same scale). Learning more about this would be helpful.