IIIT Hyderabad Publications |
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Understanding Structure of Urban Built Environment and its Implications on Movement Affinities of SpaceAuthor: Rajesh Chaturvedi Date: 2017-12-30 Report no: IIIT/TH/2017/96 Advisor:K S Rajan AbstractThe structure of built environment is defined by the collection of fundamental units contained in it. Two of the most basic units in an urban setting are Roads and Buildings. The buildings form most of the origin and destination pairs for intra-city movements and the road-networks aid these movements. The basic aim of this thesis is to identify patterns of arrangements of these elementary units in any urban agglomeration along with deriving a syntactic meaning out of the arrangement geometry and secondly, how the movements patterns and dynamic occupancy of spaces are often affected by the synergy of roads and buildings. We try to identify combinations of arrangement of these two that increase the odds in favor of increasing attractor values of spaces. In past, several studies were carried out to extract patterns in urban environment which were often driven by considering networks only. A few studies paid attention to buildings and their characteristics such as their volumetric capacities and their locations along the network. The approach we have chosen to define symmetries and regularities in structures is hybrid in nature as it considers both buildings and road-networks as well as their intrinsic interactions such that the buildings and roads in closer vicinity inherit the properties of each other. The spatial indices we used for analyzing built environments are based on navigational properties of any location which suggest accessibility of the location depending on its connectivity with neighborhoods. These indices are motivated by a combination of Space Syntax configurational theory that uses topological (Angular & Segment) distances along network and Urban Network Analysis which has metric distances as basis for accessibility computations. The accessibility based on topological as well as metric computations is defined as function of how often a spatial unit lies on the shortest paths while commuting all origin-destination pairs in its neighborhood, how many of surrounding places can be reached from this place, how compactly packed is the neighborhood for a given set of fixed radii, and do the shortest paths between origin-destination pairs resemble straightest distances. These characteristics can be discussed for both buildings and roads. We use spatial autocorrelation for our chosen accessibility indices to analyze significance of occurrence of specific patterns of neighborhoods for city of Hyderabad, India, and thus consider not only spatial units but areas as whole. We discuss the inconsistencies in methodologies used, and propose a weight metrics for urban road network, weights are driven by dimensions of buildings. This enables us to reduce computational complexity by a significant margin using road networks as the only analysis layer for making configurational computations. We verify that it is intrinsic property of spatial organization that more similar areas tend to occur in closer vicinity of each other by using K Means clustering. In the process, we also propose that the clusters formed with varying radii of analysis are suggestive of spatial properties of urban environment. Lastly, we present a data driven methodology to classify regions in terms of amount vehicular traffic. In this technique, Average Annual Daily Counts (AADT) of vehicular traffic are used to represent roads into three categories of high, medium and low which are then superimposed on buildings. The chosen spatial indices form feature vectors for training the model while the vehicular traffic categories superimposed on buildings act as prediction target labels. To get the best accuracy model, we compare performances of Logistic Regression Classification model, Decision Tree Classification model and ensemble based Gradient Boosted Classification model. The ensemble based model using gradient boosting algorithm outperforms all other models in this classification task. While discussing all methodologies to denominate areas in terms of their symmetry and constrained by relative carriage of vehicular traffic, we go from a local measure of trip distances towards global trip distances that implies we go from inspection of nearby regions in close proximity of area under observation towards larger distances. This process enables us to draw comparisons between areas corresponding to their obvious choices of being preferred for various trip distances. For example, an area being preferred for short trip distances might not be a good fit for longer trips and vice versa. The approaches presented in this can draw conclusive reports from the perspective of architectural and infrastructural planning for new city sites and modifying the existing ones. Full thesis: pdf Centre for Spatial Informatics |
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