Mapping properties for Old North Dayton

Find the neighborhood boundaries

Two potential paths:

• Find the boundaries and identify properties within the boundary
• Less exact, but potentially easier.
• Easier to visualize.
• https://liveindayton.org/map/
• https://mistythomastrout.wordpress.com/portfolio/city-of-dayton-neighborhoods-map/
• Identify the properties by some field in the data.
• Will allow for exact matching, but doesn't visualize as well

Exploring the data

• NBHD: 41000OND
• C_TRACK: 001800
• https://www2.census.gov/geo/maps/dc10map/tract/st39_oh/c39113_montgomery/DC10CT_C39113_001.pdf
• https://www.qgis.org/
• https://gis.mcohio.org/VPCore/VP.html?config=aud

The Census Tract containing Old North Dayton.

The Neighborhood code containing Old North Dayton, as seen in the QGIS map.

The Neighborhood code containing Old North Dayton, as seen on the City of Dayton's website.

Find the properties on the rental registry.

We are using the registry.json file, which we generated from our rental registry scraper.

Find the nuisance list for properties.

We are using the housing_complaints.csv file, which we downloaded from the City of Dayton's open data portal, here.

Data Processing Scripts

filter_registry.py

This script filters the registry.json file to include only those records whose parcel field matches a TAXPINNO value found in the ond-parcels.geojson file. The filtered records are saved to a new file called filtered_registry.json. This is useful for narrowing down the registry data to only those parcels present in the geojson dataset.

count_records.py

This script counts and compares the number of records in registry.json and filtered_registry.json. It prints the total number of records in each file and the percentage of records retained after filtering. This helps to quickly assess the impact of the filtering process.

merge_data.py

This script merges the filtered registry data (filtered_registry.json) with the housing complaints data (housingcomplaints_geocoded.csv). It adds a type field to each record to distinguish between registry records and complaints. The merged data is saved in merged_data.json with two arrays: one for registry records and one for complaints that match the filtered registry parcels. This enables combined analysis of property registry and complaint data.

geocode_housingcomplaints.py

This script geocodes the housing complaints data by matching complaint addresses to a coordinates lookup table (such as output.csv). It adds latitude, longitude, and TAXPINNO fields to each complaint record based on address matching, and saves the results to a new CSV file (default: housingcomplaints_geocoded.csv). This enables spatial analysis and parcel-level matching of housing complaints.

Data Files

ond-parcels.geojson

This is the geojson file containing the parcel boundaries for Old North Dayton based on the Census Tract code.

housingcomplaints.csv

This is the housing complaints data from the City of Dayton.

housingcomplaints_geocoded.csv

This is the geocoded housing complaints data with latitude, longitude, and TAXPINNO fields added.

merged_data.json

This is the merged data between the filtered registry and the geocoded housing complaints data.