Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Re-defining transport for London’s strategic neighbourhoods from spatial and social perspectives

Neighbourhoods are fundamental spatial units to present social phenomena in urban studies. Many studies use administrative boundaries such as census tracts as representations of neighbourhoods, but such boundaries may poorly represent the underlying social structures and physical attributes which might help define more vernacular conceptions and dialectical evolution of these zones. In this paper, using the goal of creating a new set of ‘Strategic Neighbourhoods’ for Transport for London (TfL) as vehicle for analysis, we evaluate two contrasting spatially and socially focused methodologies of neighbourhood generation. In comparing the outputs of a tertiary-communities (T-Communities) method and a combined Principal Component Analysis (PCA) and Minimum Spanning Tree (MST) cluster analysis method with an earlier iteration of Strategic Neighbourhoods defined by TfL, indices including neighbourhood size, intra-class correlation coefficient (ICC), and the number of community centres are calculated to evaluate their relative performance which demonstrate that both methods create neighbourhood boundaries that can better capture intra-group social homogeneity and are more suitable for analysis than the original SNA boundaries. These results are discussed in the context of the dialectic relationship between neighbourhood outcomes, spatial structures, and social characteristics, leading to more widely relevant conclusions that neighbourhood boundary delineation should combine spatial structure, social attributes, and experimental knowledge to effectively sub-divide urban activity

Urban identity through quantifiable spatial attributes: coherence and dispersion of local identity through the automated comparative analysis of building block plans

This analysis investigates whether and to what degree quantifiable spatial attrib-utes, as expressed in plan representations, can capture elements related to the ex-perience of spatial identity. By combining different methods of shape and spatial analysis it attempts to quantify spatial attributes, predominantly derived from plans, in order to illustrate patterns of interrelations between spaces through an ob-jective automated process. The study focuses on the scale of the urban block as the basic modular unit for the formation of urban configurations and the issue of spa-tial identity is perceived through consistency and differentiation within and amongst urban neighbourhoods

The grass is not always greener in the neighbor's yard:Bayesian and frequentist inference methods for network autocorrelated data

People do not live in isolation. Instead, we constantly interact with others, which affects our actions, opinions, or well-being. Throughout the last decades, the network autocorrelation model has been the workhorse for modeling network influence on individual behavior. In the network autocorrelation model, actor observations for a variable of interest are allowed to be correlated, where a network autocorrelation parameter represents and quantifies the strength of a network influence on the variable of interest. More precisely, an actor’s observation is assumed to be a function not only of a set of explanatory variables but also of the observations for the actor's neighbors, i.e., other actors in the network this actor is tied to. In this thesis, we develop a fully Bayesian framework to estimate the network autocorrelation model and to test multiple hypotheses on the network autocorrelation parameter(s) against each other. Taking the Bayesian route hereto has at least three attractive features that are not shared by classical statistical methods such as maximum likelihood estimation and null hypothesis significance testing. First, the Bayesian approach enables researchers to include previous empirical information about the network autocorrelation parameter through a prior distribution, which may attenuate the underestimation of the network autocorrelation parameter associated with maximum likelihood estimation of the model. Concomitantly, we also derive Bayesian default procedures for situations in which such prior information is completely unavailable. Second, Bayesian techniques do not rely on asymptotic approximations when estimating uncertainty and performing inference about the network autocorrelation parameter but yield accurate results even in case of small networks. Third, using Bayes factors as opposed to null hypothesis significance testing, researchers can test any number of hypotheses on the network autocorrelation parameter and quantify the amount of relative evidence in the data for each tested hypothesis. We provide several such Bayes factors and generalize the presented methodology to test order hypotheses on multiple network autocorrelation parameters, representing the strength of multiple influence mechanisms that may have some connection to the variable of interest. Furthermore, we introduce a discrete exponential family model to analyze network autocorrelated count data for which the network autocorrelation model itself is not well-suited. This novel model permits principled statistical inference without making any potentially limiting distributional assumptions on the marginal or conditional counts but is flexible enough to accommodate a wide range of count patterns. In sum, the methods developed in this thesis allow researchers studying network influence to quantify and test the strength of network influence(s) on a variable of interest in ways that go beyond the current state of the art

Spatial autocorrelation analysis in plant population: An overview

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given

Doctor of Philosophy

dissertationData-driven analytics has been successfully utilized in many experience-oriented areas, such as education, business, and medicine. With the profusion of traffic-related data from Internet of Things and development of data mining techniques, data-driven analytics is becoming increasingly popular in the transportation industry. The objective of this research is to explore the application of data-driven analytics in transportation research to improve the traffic management and operations. Three problems in the respective areas of transportation planning, traffic operation, and maintenance management have been addressed in this research, including exploring the impact of dynamic ridesharing system in a multimodal network, quantifying non-recurrent congestion impact on freeway corridors, and developing infrastructure sampling method for efficient maintenance activities. First, the impact of dynamic ridesharing in a multimodal network is studied with agent-based modeling. The competing mechanism between dynamic ridesharing system and public transit is analyzed. The model simulates the interaction between travelers and the environment and emulates travelers' decision making process with the presence of competing modes. The model is applicable to networks with varying demographics. Second, a systematic approach is proposed to quantify Incident-Induced Delay on freeway corridors. There are two particular highlights in the study of non-recurrent congestion quantification: secondary incident identification and K-Nearest Neighbor pattern matching. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. Lastly, a high-dimensional clustering-based stratified sampling method is developed for infrastructure sampling. The stratification process consists of two components: current condition estimation and high-dimensional cluster analysis. High-dimensional cluster analysis employs Locality-Sensitive Hashing algorithm and spectral sampling. The proposed method is a potentially useful tool for agencies to effectively conduct infrastructure inspection and can be easily adopted for choosing samples containing multiple features. These three examples showcase the application of data-driven analytics in transportation research, which can potentially transform the traffic management mindset into a model of data-driven, sensing, and smart urban systems. The analytic