Introduction

In urban planning and development, geographical information systems (GIS) are indispensable tools for visualizing and interpreting spatial data. My recent project involved a comprehensive analysis of housing types in the Manchester region of the United Kingdom, employing QGIS—a leading open-source GIS software.

Objective

The goal was to map the distribution of various housing types—flats, maisonettes, terraced, semi-detached, and detached homes—across the Manchester region. This visual representation aims to identify patterns and density of housing types, providing valuable insights for urban planners, real estate developers, and policy-makers.

Methodology

The data was sourced from the Price Paid Data and Office of the National Statistics, which includes postcodes, housing types, and their geographical coordinates. This data was processed and cleaned up in Microsoft SQL Server and was converted to GeoSpatial Point to be used as Views which can be imported into QGIS for Data Visualization. After importing the data into QGIS, I applied thematic mapping techniques to differentiate the housing types using a colour-coded system. The density of housing in each area was represented through a heat map overlay, which helped identify the high-concentration areas.

Results

The resulting maps (as seen in the provided screenshots) reveal a clear pattern of housing distribution. The heat map indicates that flats and maisonettes are densely packed in the city centre, gradually transitioning to more terraced houses, followed by semi-detached and detached homes in the outskirts.

  • Flats and Maisonettes: Predominantly concentrated in the city centre, reflecting higher population densities and the urban lifestyle.
  • Terraced Houses: Evenly spread throughout the region, these represent the traditional working-class homes.
  • Semi-detached and Detached Homes: Mostly found in the suburbs, indicating preferences for larger living spaces and family-oriented environments.

Discussion

The analysis uncovers not only the current housing landscape but also the socio-economic fabric of the Manchester region. The concentration of flats in the city centre correlates with a younger, perhaps more transient population, while the outskirts, with more spacious housing, cater to families and longer-term residents.

These insights could be vital for addressing housing shortages, improving infrastructure, and developing new housing policies. The clear distinction in housing types across regions also offers clues to historical development patterns and future growth trajectories.

Conclusion

This GIS project highlights the powerful capabilities of QGIS in transforming raw data into actionable insights. The visualizations serve as a narrative telling the story of a city’s growth, people, and living preferences. As urban areas evolve, such analyses become crucial in shaping sustainable and inclusive urban spaces.