This project involved a multi-stage Geographic Information System (GIS) analysis in QGIS to identify and prioritize optimal locations for new accessible green spaces within underserved residential areas of Queens, New York City. The primary objective was to pinpoint areas of significant need and then identify suitable, available parcels of land within those areas.
Here's a breakdown of the key components and processes:
I. Data Acquisition and Preparation:
Residential Areas Data: Initial geographic data representing residential zones in Queens was acquired.
Existing Green Spaces Data: A comprehensive layer of existing accessible green spaces in Queens was obtained (likely from NYC Parks or similar sources).
Parcel Data (NYC PLUTO): Crucial detailed property information for Queens was downloaded from NYC Open Data (specifically the PLUTO dataset), providing attributes like land use, ownership type, number of buildings, and lot area.
Base Map: A clean, dark gray, and google imagery basemaps were integrated to provide clear geographic context.
II. Spatial Analysis and Identification of Underserved Areas:
Buffer Analysis: Buffers (specifically, a 0.5-mile radius) were created around all existing accessible green spaces.
Spatial Selection/Overlay: Residential areas that did not intersect with these green space buffers were identified. These areas represent the "underserved residential areas" – the core focus of the project.
Layer Refinement: The resulting "underserved residential areas" were processed (potentially dissolved or merged) to create a clear, consolidated layer representing the primary areas of need for green space intervention.
III. Site Suitability Analysis for Optimal Green Space Locations:
Parcel Filtering and Identification of Available Land:
The comprehensive Queens parcel data (PLUTO) was used.
An attribute-based selection was performed to identify potentially available or convertible land, focusing on:
LANDUSE: Filtering for vacant land, parking facilities, or other underutilized land use types
LOTAREA: Filtering for parcels meeting a minimum size threshold (e.g., 5,000 sq ft) to ensure viability for a meaningful green space.
Spatial Intersection with Underserved Areas: The "available" or "buildable" land parcels were then spatially intersected with the "underserved residential areas" layer. This ensured that only potential green space sites located directly within the identified areas of need were considered.
Attribute Enrichment: Key PLUTO attributes (like OWNERTYPE and refined LANDUSE) were joined back to the final candidate sites to provide essential information regarding ownership and current usage, crucial for assessing feasibility.
Visualization:
Thematic Mapping: The final map visually highlighted the identified underserved areas and the specific, prioritized candidate sites within them using distinct symbology and clear legends.
