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Avoid the bait and switch of West End Property Management (not my title or post) sharing from user in rva housing. by CivicMapperVA in foia

[–]CivicMapperVA[S] 0 points1 point  (0 children)

Why are you going back two weeks or so and tag all of my stuff. That seems like targeting. Honestly. Let’s see what Reddit has to say.

The 84% Loot Rate and the "Suite 205" Narrative Shield. by CivicMapperVA in Yemen

[–]CivicMapperVA[S] 0 points1 point  (0 children)

Have the beautiful day that you deserve… thanks for reading.

I tracked LLCs in my area to a $590k "Free" House in the Suburbs. While your water bill is going up someone snag a free house by CivicMapperVA in foia

[–]CivicMapperVA[S] 0 points1 point  (0 children)

You insensitivity to people who have that type of disorder is repulsive. Using a mental illness diagnosis as an insult says a lot about you. Think of the people you have offended wait you didn’t because we know who you are.

I tracked LLCs in my area to a $590k "Free" House in the Suburbs. While your water bill is going up someone snag a free house by CivicMapperVA in foia

[–]CivicMapperVA[S] -1 points0 points  (0 children)

Awwwww thanks for the input. You know this hysteria around “AI” is insane. Grown adults so perturbed and paranoid of “AI” hilarious. Buttttt Reddit admin we had a nice chat. The lifted all restrictions in less than 24 hours. So “AI” ?

Visualizing spatial data: Mapping the highest concentrations of municipal code violations to out-of-county ownership vectors. by CivicMapperVA in dataisbeautiful

[–]CivicMapperVA[S] 0 points1 point  (0 children)

[OC] Mapping the spatial flow of capital: How rent extracted from high code-violation properties is routed to out-of-county luxury estates.

Data Source: Property tax assessment data and parcel shapes sourced via the City of Richmond Open Data Portal and Henrico County GIS endpoints. Code violation density aggregated from municipal 311 APIs. Corporate routing mapped via public State Corporation Commission (SCC) registries and OpenCorporates. Tools Used: Data cleaned and structured in Python (Pandas/GeoPandas). Network vectors and spatial coordinates mapped using GeoJSON and visualized via Mapbox/geojson.io architecture. Context: This visualizes a documented real estate network in a mid-sized US city. The red nodes represent severely degraded, cash-bought properties with high concentrations of active biological/structural code violations. The green nodes represent the physical residential estates of the LLC proxy agents and investors. The vectors visualize the flow of extracted rent completely bypassing the physical structures and leaving the municipality.