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Spatial Modelling + GIS
Skills module:
Contents
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. MACRO ANALYSIS 1.1. Congestion + Polarization 1.2. Noise + Air Pollution 1.3. Integration + Industrialization 1.4. Macro overlay
. MICRO ANALYSIS 2.1. Network Analysis 2.2. Visual Graph Analysis 2.3. Combinatory Map 2.4. Isovist Analysis
. CONCLUSION 3.1. Conclusion 3.2. References
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### 19131026 This report includes work done as part of the Spatial modelling and GIS skill module to showcase the level of understanding and applicability of different layers of data processing. The submission for this module consists of two parts. In the first part titled “Macro analysis”, I showcase the process of site identification from a large scale map of London. Based on the concept of Urban Fragmentation, I identify several key contributing factors. In this part, I demonstrate my ability to gather, generate and process data with the help of QGIS. After developing maps to visualize the gathered datasets, I attempt to generate Kernel density heat maps and contours for each of the factors to identify areas of high density. As the final step for identification, all the density maps are combined and overlayed on a grid to identify areas of maximum overlapping.
Tutor: Anna Kampani
List of softwares
The second part of the report titled “Micro analysis” is divided into three parts: Network analysis, Isovist analysis and Visibility graph analysis. In this part, I attempt to demonstrate my ability to model, analyse and interpret three different spatial modelling methods on different scales and radii with the use of depthmapX, QGIS and Grasshopper.
Rhino 6 Grasshopper DepthmapX Qgis 3.0
Plugins
For this part, I take the identified site from the macro analysis to perform an in-depth spatial analysis.
Decoding spaces
“Easiest way to predict the future is to create it.”
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1.1 CONGESTION + POLARIZATION
Building Heights Railway Lines Road Network
1.1.1 2000m
1.1.2
1.1.3 2000m
2000m
1.1.1. DATASETS
1.1.2. CONGESTION
1.1.3. POLARIZATION
The datasets used for analysis: Congestion: 1] Road Network 2] Rail Network Polarization: 3] Building Heights
The map above illustrates the congestion in the city network. “Intersection points” between the road and rail network layers are extracted and analysed by generating a kernel density heat map.
The map above illustrates Polarization in the city fabric. “Centroids” of tall buildings above 35m are extracted and analysed by generating a kernel density heat map.
Sources: 1] data.gov.uk 2] data.gov.uk 3]OS Building Heights - Digimapdigimap.edina.ac.uk
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1.2 NOISE + AIR POLLUTION
Rail Noise Class Road Noise Class Air pollution
1.2.1 2000m
1.2.2
1.2.3 2000m
2000m
1.2.1. DATASETS
1.2.2. NOISE POLLUTION
1.2.3. AIR POLLUTION
The datasets used for the analysis: Noise pollution: 1] Road Noise Class 2] Rail Noise Class Air Pollution: 3] P.M 2.5 value
The map above illustrates the noise pollution in the city. The noise class polygons are densified with points and analysed by generating a kernel density heat map.
The map above illustrates the air pollution in the city. Points of high PM 2.5 values are selectedand kernel density heat maps are generated.
Sources: 1] data.gov.uk 2] data.gov.uk 3] data.london.gov.uk
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1.3. INTEGRATION + INDUSTRIALIZATION
Integration r3200 Industrial Zones
1.1.1 2000m
1.1.3
1.3.1 2000m
2000m
1.3.1. DATASETS
1.3.2. INTEGRATION
1.3.3. INDUSTRIALIZATION
The datasets used for the analysis:
The map above illustrates the level of integration in the city network. Angular Segment Analysis is run on the street network and segments with low integration values are identified. These segments are then used to generate the kernel density map.
The map above illustrates the density of industrial zones in London.
Integration: 1] Integration r3200m Industrialization: 2] Industrial land-use Sources: 1] depthmapX Angular segment analysis 2] data.london.gov.uk
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1.4. MACRO OVERLAY
1.1.1
IDENTIFIED SITE
1.1.2
1.2.1
1.2.2
1.3.1
1.3.2
1.4.1
2000m
SITE IDENTIFICATION The map above illustrates a grid with all the combined densities. The point are assigned to the grid and the regions with highest density are highlighted. The identified regions satisfies all the factors leading to Urban fragmentation and thus provides an ideal site for intervention.
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2.1 NETWORK ANALYSIS
CHOICE
1000m
INTEGRATION
1000m
RADIUS 600m
RADIUS 1200m
RADIUS 2400m
The maps above illustrates Choice (betweenness centrality) and Integration (closeness centrality) analysis respectively of the King’s cross street network using DepthmapX for a radius of 600m. This value of radii relates to pedestrian movement.
The maps above illustrates Choice (betweenness centrality) and Integration (closeness centrality) analysis respectively of the King’s cross street network using DepthmapX for a radius of 1200m. This value of radii relates to bicycle movement.
The maps above illustrates Choice (betweenness centrality) and Integration (closeness centrality) analysis respectively of the King’s cross street network using DepthmapX for a radius of 2400m. This value of radii relates to vehicular movement.
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Streets with highest values
Streets with lowest values
0m
0m
0 20
0 20
BETWEENNESS CENTRALITY
CLOSENESS CENTRALITY
The exploded isometric view above illustrates Choice (betweenness centrality) analysis of the King’s cross street network using Decoding spaces tool in Grasshopper.
The exploded isometric view above illustrates Integration (closeness centrality) analysis of the King’s cross street network using Decoding spaces tool in Grasshopper.
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2.2 VISIBILITY GRAPH ANALYSIS
RADIUS 100m
1000m
RADIUS 200m
1000m
VISUAL INTEGRATION
ISOVIST AREA
VISUAL CONNECTIVITY
The maps above illustrates Visual Integration of the King’s cross region using DepthmapX for radius of 100m and 200m.
The maps above illustrates Isovist Area of the King’s cross region using DepthmapX for radius of 100m and 200m.
The maps above illustrates Visual Connectivity of the King’s cross region using DepthmapX for radius of 100m and 200m.
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2.3. COMBINAVTORY MAP
Highest Integration value
Lowest Integration Value
Visual Integration per Building
Visual Integration 200m radius
COMBINATORY EXERCISE
The exploded isometric view above illustrates the combinatory use of DepthmapX, QGIS and Grasshopper. The Visual Integration is calculated using depthmapX. These values are mapped in QGIS and joined with the buffered building layer. The map is then exported to Grasshopper and converted into a point cloud based on the Visual Integration values.
0m
0
20
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2.3 ISOVIST ANALYSIS
LOCATION 1
LOCATION 2
PATH 1
PATH 2
PATH 3
LOCATION 3
ISOVIST 2D
ISOVIST 3D
The maps above illustrates the isovist 2d analysis done on six points each along 3 different paths in the King’s cross area. This was executed using the isovist tool on Grasshopper.
The maps above illustrate isovist 3d analysis on 3 public squares in the site. The building are colour coloured based on the number of isovist rays falling on them. This exercise was done using the decoding spaces tool in Grasshopper.
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3.1 CONCLUSION
This report includes work done as part of the Spatial modelling and GIS skill module to showcase the level of understanding and applicability of different layers of data processing. QGIS allows in acquiring spatially indexed data from variety of sources, changing the data into useful formats, storing the data, retrieving and manupulating the data for analysis, and then generating the output required by a given user. Its greatest strength is based on the abilly to handle large, multilayered, heterogeneous databases and to query about the existence, location and properties of a wide range of spatial objects in an interactive way. The lack of analytical and modelling functionality is, however, widely recognised as a major deficiency of current systems. But a fluid ecosystem between msodelling softwares like Grasshopper and analytical softwares like depthmapX clearly overcomes this deficit. In this skill module report, I tried to present some of the fundamental applications of the design tools in spatial modelling and Geographic Information System on a both larger and smaller scale. The larger scale analysis was aimed at focusing on major urban problems and identifying a site for an intervention. The smaller scaler however focused on this identified site to understand its spacial and geographic characteristics. The analytic tools and methodologies presented in this report strengthens the design claim and spatial understanding.
3.2 REFERENCES 1] data.london.gov.uk 2] toolbox.decodingspaces.net 3] digimap.edina.ac.uk 4] data.gov.uk 5] Open Street Map 6] RC14 skill workshops 1-4 pdf by Anna Kampani
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