Exploring Urban Micro-Agglomeration Patterns
Jane Jacobs, extending on ideas proposed by Warren Weaver, suggested that cities are problems of organized complexity. This view of the city embraces organic agglomeration, diversity, and proximity, and presents the challenge of interpreting and assessing this complexity, especially on a local-scale.
placeSpace provides a lens to explore the complexity of the urban environment by spatial distribution and agglomeration of amenities at a local urban scale. Through this interactive data visualization tool, users can gain a better understanding of how amenities agglomerate, co-locate and define areas within the city.
The platform currently only supports London, but can be extended to analyze other cities.
The presence of different amenities has become an important aspect of livable and walkable cities. Their location and distribution can tell about the environmental, social, and economic aspects of a particular place.
On the graph bellow you can visualize the how the total number of amenities in the city compare with each other. On the right, a heat map is shown representing the concentration of amenities.
- Hover over an element to see its total count.
- Click on an element to see its distribution over London.
- To Control level of detail, click on buttons on bottom of graph.
To identify local scale agglomeration patterns different methods can be used, each yielding different results based on how each method identifies a spatial cluster. Here two density clustering methods are explored: DBSCAN and HDBSCAN. Click on the buttons bellow to see how they differ.
HDBSCAN produces more homogeneous clusters, and their boundaries seem to correspond well to known areas in the city. For the rest of the analysis the results from this clustering method are used.
Total Number of Areas Identified :
After identifying areas by amenity agglomeration, we can examine how different amenities tend to co-locate with each other. The network graph bellow shows amenities linked by the probabilities that they are in the same area. This probability is calculated by a Spearman Rank Coefficient (rho).
Click enlarge to view full screen.
Using a hierarchical clustering method, we have identified 12 different clusters and have named them based on the top occurring amenities in each. Although the clustering was applied only using amenity counts the results show a clear spatial pattern.
Hover over the Map to get variables associated with each area.
What is the relationship between diversity and other variables?
The graph below shows the correlation between amenity diversity in each area (measured by Shannon entropy index) to public transport accessibility, job / residential population ratio and medium income.
Hover over the points to see their location on the map.