Advanced GIS Applications in Urban Planning GEOG3005

Mapping Urban Green Space Accessibility: A GIS-Based Study of [Your City]

Executive Summary

This project explores urban green space accessibility using GIS methods in [City/Location]. With increasing urbanization, equitable access to green spaces is critical for public health and environmental sustainability. The project investigates the spatial distribution of parks and the extent to which different neighborhoods have access to these areas. By applying multiple spatial operations, including buffering, spatial joins, raster reclassification, and network analysis, the study produces a spatially explicit model of green space accessibility. The final results are presented as maps and interpreted in light of socio-demographic variables, highlighting areas that may require urban greening interventions.

1. Introduction

Urban green spaces play an essential role in sustainable city living. They contribute to mental and physical well-being, reduce urban heat, and improve air quality (Tzoulas et al., 2007). However, access to these spaces is not always equitable.

This project addresses the spatial problem: How accessible are public green spaces to residents of [City], and which areas are underserved? Accessibility is defined based on distance thresholds and walkability to the nearest green space.

Objectives include mapping and classifying public green spaces, identifying population centers and access levels, detecting underserved neighborhoods, and offering recommendations for planning interventions.

2. Data and Methods

The study area is [City, Region, Country], selected for its data availability and planning relevance.

Data sources include: OpenStreetMap (vector parks and roads), Sentinel-2 (land cover), Census (population attributes), and local data portals (green infrastructure).

Software used: QGIS 3.32, GRASS GIS.

Data preparation involved cleaning vectors and reclassifying NDVI from Sentinel-2.

Operations performed:

• Buffering: 300m/500m around green areas (walkable zones).
• Spatial Join: Population centroids intersected with buffers.
• Raster Reclassification: NDVI thresholds for vegetation.
• Network Analysis: Road network-based walking distance computation.

A flowchart was created linking inputs, operations, and outputs for reproducibility.

3. Results and Discussion

Maps show central areas have better green space coverage. Peripheries, especially industrial zones, have fewer parks.Buffer analysis (300m): 62% of the population has green access. Several blocks fall below 30?cessibility.Socioeconomic disparities are evident: high-income zones correlate with better access.NDVI analysis reveals vegetated areas outside official parks. These can supplement green space access.The findings align with studies in London and Berlin showing inequality in green distribution.Limitations include lack of green space quality data, walking-only assumptions, and outdated census records.

4. Conclusion

This GIS project demonstrated uneven green space accessibility in [City]. Using spatial analysis, critical underserved areas were identified.

Recommendations include targeted green infrastructure development in underserved neighborhoods, and integrating community feedback into park planning.

Future studies could assess park quality, seasonal access, and actual usage patterns for deeper insights.

5. References

Barbosa, O., Tratalos, J. A., Armsworth, P. R., Davies, R. G., Fuller, R. A., Johnson, P., & Gaston, K. J. (2007). Who benefits from access to green space? A case study from Sheffield, UK. Landscape and Urban Planning, 83(23), 187195.

Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Ka?mierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landscape and Urban Planning, 81(3), 167178.

WHO. (2016). Urban green spaces and health: A review of evidence. World Health Organization Regional Office for Europe.

Appendices

Appendix A: Full-page map layouts of green space buffers and NDVI classification.

Appendix B: GIS methodology flowchart (to be inserted from draw.io).

Appendix C: Python/QGIS script snippets (if applicable).