How can you identify urban development patterns with spatial analysis?

1 Why spatial analysis?

Spatial analysis can help you answer questions such as: How dense is the urban population? How far are people from the city center? How connected are different urban zones? How diverse are the land uses and activities? How efficient are the transportation and infrastructure networks? How equitable are the access and opportunities for different groups of people? These questions can help you evaluate the performance, quality, and sustainability of urban development patterns and inform your planning decisions and policies.

2 What data do you need?

To perform spatial analysis, you need geographic data that represent the urban features and attributes that you want to study. Geographic data can be either vector or raster. Vector data are composed of points, lines, and polygons that have coordinates and attributes. For example, you can use vector data to represent buildings, roads, parks, parcels, or census blocks. Raster data are composed of cells or pixels that have values. For example, you can use raster data to represent elevation, land cover, population density, or satellite imagery.

3 How to measure urban development patterns?

Spatial analysis is a useful tool for measuring urban development patterns, depending on the objectives and data availability. Sprawl, for example, is the expansion of urban areas over low-density and dispersed land, and can be calculated by measuring the urban extent, the average density, the fragmentation index, or the entropy index. Compactness is the concentration of urban areas over high-density and contiguous land, and can be measured by calculating the shape index, the contiguity index, the compactness ratio, or the fractal dimension. Polycentrality is the distribution of urban areas over multiple centers or subcenters and can be measured by identifying the urban centers, the subcenters, the centrality index, or through network analysis. Fragmentation is the separation of urban areas by natural or artificial barriers and can be measured by calculating the connectivity index, the isolation index, the patchiness index, or landscape metrics.

4 How to visualize urban development patterns?

Visualizing urban development patterns can help you communicate and interpret the results of your spatial analysis. You can use different types of maps and charts to display your data and indicators, such as choropleth maps that show the variation of a variable by coloring different geographic units, dot density maps that show the distribution of a variable by placing dots over a geographic area, kernel density maps that show the intensity of a variable by smoothing and interpolating the values over a geographic area, and scatter plots that show the relationship between two variables by plotting them on a horizontal and vertical axis. For example, you can use a choropleth map to show population density or land use diversity, a dot density map to show the location and concentration of urban population or jobs, a kernel density map to show hotspots and coldspots of urban activity or accessibility, and a scatter plot to show correlation between urban density and distance from the city center or between urban fragmentation and connectivity.

5 How to apply spatial analysis in urban planning?

Spatial analysis can help you apply urban planning principles and practices in different contexts and scales. It can enable you to assess current and future trends of urban development and their impacts on the environment, economy, society, and culture. Furthermore, spatial analysis can be used to compare the performance and quality of different urban areas or regions with spatial indicators and metrics. Additionally, it can help identify the strengths, weaknesses, opportunities, and threats of different urban development patterns and their spatial implications. Moreover, it can be used to design and evaluate alternative urban development strategies and policies that can enhance the sustainability, resilience, livability, and equity of urban areas. Finally, spatial data and maps can be used as a common language and platform to engage with different stakeholders and communities.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?