G.I.S
Geographical Information Systems TRP627 GIS for Built Environment Professionals Geovisualisation Report | May 2020
Adish Siddapur Matada Reg. no. 190183475
Visualization 1 Visualization 2
Slope Climb Efforts W.R.T. Existing Roads The Visualization The Methodology The Topic Data and Method Analysis and Interpretations Limitations References
TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Slope Climb Efforts W.R.T. Existing Roads : The Visualization GIS for Built Environment Professionals
Fig 1: Slope Climb Efforts Map, Sheffield TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Slope Climb Efforts W.R.T. Existing Roads : The Methodology GIS for Built Environment Professionals
OS Geodata Pack: CDRC 2015 Roads (Lines) Roads(Buffer10.00)
LIDAR Data
Roads (Diffuse)
2m DTM for Site Merge all Raster Tiles
Layer Clipped to Sheffield Extent Colour: White (For visual styling)
Slope Tool: Degrees Raster Clipped by Mask Layer
Road only Slope Layer Raster to Vector Change Symbology to Category Category Conditions (Slope Conditions) changed based on texts/Reading
0.00 - 1.15: 1.15 - 3.00: 3.00 - 5.50: 5.50 - 90.0:
Flat Mild Slope Steep Not Suitable
Road Vector Styling: Drop Shadow New Layer Created Adjust Page Setup Layout
Map Insert Picture Insert
Image Export
Legend TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Slope Climb Efforts W.R.T. Existing Roads GIS for Built Environment Professionals
The Topic “Walking and cycling specifically can help overcome some of the societies’ current challenges like energy consumption, climate change, an aging population, and increasing scarcity of land. For this reason, the relationship between pedestrian activity and various influence factors of the built environment is of great interest.” (Meeder, Aebi and Weidmann, 2017) The effort of traveling from one point to another is perceived by the distance traveled and the elevation lost or gained. The slope plays a major role in the effort need to cover a certain distance. A Geo-visualized graphic indicating effort needed to ascend or descend certain roads can help users navigate their ways through the city by foot, bike, or a wheelchair based on their physical abilities.
Fig 2: 2m DTM for Sheffield
Fig 3: DTM clipped to Mark Layer (Existing road - CDRC 2015)
Fig 4: Category Conditions (Slope Conditions)
Fig 5: Vectorized Road DTM Categorized Colour scheme
Data and Method Data Source: The underlining data used for this visualization is the Digital Terrain Model (DTM) and the Road markings for the city of Sheffield. The DTM was extracted from ’LIDAR - Digimaps’ (Fig 2) and the road line marking from the ‘2015 CDRC Ordinance Survey geodata pack’. Cartographic and Analytical techniques: The Road line layer was given a ‘Buffer’ of 10.00 to get thickness to the road. These lines were ‘Diffused’ to arrive at a vector polygon representing the road (Fig 3). The DTM tiles were merged into a single layer and run through the ‘slope tool’, to form a slope representing raster (in degrees). This raster was clipped by masking the ‘Diffused road’ Layer. The resulting layer was converted into a vector that was symbolized based on the category of slopes (Fig 4). Visualization Choices: For a user to situate themselves to the city, the generated visualization was over-layed on an ‘open street map’ of the city. As the over-layed map wasn’t highlighting enough, a secondary vector layer of the road was placed below the visualization. The shadow dropped from it helped highlight the geo-visualization while also allowing us to read the map below. In the Layout set up, a legend was added to help understand the map. A .png file of a north symbol was added to help orient direction. TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Slope Climb Efforts W.R.T. Existing Roads GIS for Built Environment Professionals
Analysis and interpretation The Visualization shows roads of Sheffield color-coded in 4 categories based on the slope of the road. The colors indicate “Flat (0 - 1.15 Degrees), Mild Slope (1.15 – 3 Degrees), Steep Slope (3 – 5.5 Degrees), and non-suitable slopes for walking and cycling (5.5 – 90 Degrees)” (Meeder, Aebi and Weidmann, 2017). This graphic was over-layed on an Open Street Map to allow the user to situate themselves in the city. This Data helps the reader understand the walkability of these roads, which in turn can help them plan their routes around the city better. Based on personal physical capabilities, one can choose their routes and levels of climb difficulty. In terms of design opportunities, this map can also be used to appropriately propose roads and urban designs around these regions.
Limitations Due to the use of larger grid sizes (to reduce processing time), the accuracy in the slops, specifically in the regions of winding roads and hairpin bends are slightly inaccurate. This error can be avoided by using a more detailed DTM/DEM imaging.
References Szypuła, B., 2019. Quality assessment of DEM derived from topographic maps for geomorphometric purposes. Open Geosciences, 11(1), pp.843-865. Daniel, P and Burns, L orcid.org/0000-0003-0164-3668 (2018) How steep is that street? Mapping ‘real’ pedestrian catchments by adding elevation to street networks. Radical Statistics (121). pp. 26-48. ISSN 02686376 Archtoolbox.com. 2020. Calculating Slope And Common Slopes In Architecture. [online] Available at: <https://www.archtoolbox.com/representation/geometry/slope.html> [Accessed 27 May 2020]. G. Vredenburgh, A., Hedge, A., B. Zackowitz, l. and M. Weln, J., 2009. Evaluation of wheelchair users’ perceived sidewalk and ramp slope: effort and accessibility. Journal of Architectural and Planning Research, Vol. 26(No. 2), pp.145-158. Meeder, M., Aebi, T. and Weidmann, U., 2017. The influence of slope on walking activity and the pedestrian modal share. Transportation Research Procedia, 27, pp.141-147.
*Figures All figures generated by author on QGIS for the Geo Visualization report. TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Visualization 1 Visualization 2
Land Use Map for Water Runoff Calculation The Visualization The Methodology The Topic Data and Method Analysis and Interpretations Limitations References
TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Land Use Map for Water Runoff calculation : The Visualization GIS for Built Environment Professionals
Forest Meadow Residential High Density Fig 6: Land Use map for Rain Water Runoff Calculation TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Land Use Map for Water Runoff calculation : The Methodology GIS for Built Environment Professionals
LIDAR Data 50cm DTM for Site Merge all Raster Tiles Raster to Vector Conversion Vector Colour coded: Graduated Draw new layer polygon based on water flow analysis (Surface drain from hill to river) from Contour Map
Open Street Maps Data Land Use Maps CLP Layer Clip Landuse Layer with Drawn Polygon
Edit Attributes to Required bifurcation Forest (C = 0.25)
Meadow (C = 0.5)
Residential (C = 0.54)
High Density (C = 0.88)
Forest
Allotment Grass Park Scrubs
Residential
Commercial Industrial
Raster Colour Coded: Category
Add Area Column by Edit Feature in attribute table Export to Excel(CVS File) using MMQGIS Plugin Get Areas for calculation and analysis New Layer Created TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Land Use Map for Water Runoff calculation GIS for Built Environment Professionals
The Topic “The risk of urban flooding is increasing as a result of rapid urbanization. Green infrastructure (GI) is an emerging planning and design concept to mitigate urban flooding. A community-scale simulation model can be developed to quantify the effectiveness of GI on reducing the volume and peak flow of urban flooding.”(Liu, Chen and Peng, 2014) As a result of urbanization, large chunks of green land parcels are built on to form non-permeable surfaces, and this leads to an increase in the rate and volume of rainwater surface runoff. Several Urban interventions are based on the concept of capturing this Surface runoff water before it reaches a valley where it might end up flooding. To come up with appropriate design solutions, it is essential to understand the amount of runoff displaced. This study requires a broad understanding of the current land use plan and the areas of isolated spaces. By applying a formula: “Q=CiA”, where Q is the runoff quantity, C is the Coefficient of runoff (Based on the type of land use), I is the intensity of rain and A is the surface area of particular land use. With QGIS, the aim is to isolate a particular section of the site based on its slope runoff and find its surface area of different types of land uses.
Fig 7: Graduated Colour elevations
Fig 8: New Polygon Layer surface run off area
Fig 9: Land Use Map
Fig 10: Clipped Landuse Map Category Based colour
Data and Method Data Source: The underlining data for this study is the Digital Terrain Model and the Land-use Map of the selected site. The DTM is extracted from LIDAR-Digimaps and the Land-use map is from Geofabrik’s Open Street Map data. Cartographic and Analytical techniques: The Digital Terrain Model is vectorized and symbolized to represent the elevation (and contour) of the Site (Fig 7). Based on reading (Condon and Maxwell, 2015), the areas under consideration for calculation is isolated (Fig 8). This isolated area is marked as a polygonal Shapefile which is then used to Clip the Land use map to the isolated area (Fig 9/10). For the ease of calculation, the Land-use layers attribute table is then modified using the edit attribute feature to compile all uses into 4 categories (Forest, Meadow, Residential and High Density). With the Field Calculator, a column for ‘Surface areas’ is added. This allows us to individually view the surface areas of each space. The attribute table is then exported to an Excel File (CSV File) using the MMQGIS plugin for further calculations and analysis(Fig 11). TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Land Use Map for Water Runoff calculation GIS for Built Environment Professionals
Visualization Choices: For visual purposes, the isolated land use map is symbolized categorically to demarcate each part of every chuck of the land with different colors based on its land use (Fig 10). For the ease of situated on the site, the visualization with reduced opacity is overlayed on an Open Street Map.
Analysis and Interpretation
Fig 11: Excel (CSV Flie) for further calculation and analysis
The areas derived can then be applied in the formula to arrive at the rational Surface Runoff (as shown in Fig 11). This data will help Urban designers and hydrology experts analyze and interpret the impacts of certain interventions.
Limitation The areas arrived in the attribute table are arrived at considering land use areas in two dimensions. As a steeply sloped site, these areas, in reality, will be higher. For accurate areas, it will be essential to calculate the areas of the Digital Terrain Model. However, this method of calculation can still be used to arrive at an approximate value.
TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada
Land Use Map for Water Runoff calculation GIS for Built Environment Professionals
References Baiamonte, G., 2019. A rational runoff coefficient for a revisited rational formula. Hydrological Sciences Journal, 65(1), pp.112-126. Chen, J., Hill, A., and Urbano, L., 2009. A GIS-based model for urban flood inundation. Journal of Hydrology, 373(1-2), pp.184-192. Condon, L., and Maxwell, R., 2015. Evaluating the relationship between topography and groundwater using outputs from a continental-scale integrated hydrology model. Water Resources Research, 51(8), pp.66026621. Docs.qgis.org. 2020. 4.3. Lesson: Classification. [online] Available at: <https://docs.qgis.org/2.8/en/docs/ training_manual/vector_classification/classification.html> [Accessed 27 May 2020]. Liu, W., Chen, W., and Peng, C., 2014. Assessing the effectiveness of green infrastructures on urban flooding reduction: A community-scale study. Ecological Modelling, 291, pp.6-14. Zhang, B., Xie, G., Zhang, C., and Zhang, J., 2012. The economic benefits of rainwater-runoff reduction by urban green spaces: A case study in Beijing, China. Journal of Environmental Management, 100, pp.65-71.
*Figures All figures generated by author on QGIS for the Geo Visualization report. TRP627 GIS for Built Environment Professionals
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Adish Siddapur Matada