Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"

<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>&quot;Sampling location names and coordinates.csv&quot;</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>&quot;Dissemination Block Populations.csv&quot;</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>&quot;Daily average concentrations by site and pollutant.csv&quot;</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to &quot;Sampling location names and coordinates.csv&quot; to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>&quot;Jain et al - GeoHealth - Kriging Script.ipynb&quot;</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>&quot; Jain et al - GeoHealth - Kriging Script.pdf&quot;</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>