THE VARIED IMPACT OF GREENWAYS ON RESIDENTIAL PROPERTY VALUES IN A METROPOLITAN, MICROPOLITAN, AND RURAL AREA: THE CASE OF THE CATAWBA REGIONAL TRAIL

This paper presents hedonic analyses designed to estimate the real estate premium from improved access to a regional greenway system in three distinct counties. The hypothesis is tested that unobservable factors relating to the overall economic structure of each county influence how and to what extent access to open space is effectively capitalized into residential sales prices.Land Economics/Use,

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

[2017-2018 UNCG University Libraries Open Access Publishing Fund Grant Winner.] The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption

LEVERAGING GEOSPATIAL INTELLIGENCE TO ENHANCE DALLAS FIRE DEPARTMENT PROGRAMS AND DATA ANALYSIS

This research incorporates and enhances the use of geospatial intelligence, also known as a geographic information system (GIS), within the Dallas Fire Department bureaus, programs, and data analysis. Traditionally, fire departments have primarily leveraged GIS for its cartography (map) functions to produce large area wall maps of the city and first-up response areas, and for creating map books kept in fire apparatus units. While cartography is a useful tool for visualizing broad information, incorporating other aspects of GIS into additional areas of the department will provide heightened situational awareness and can become a primary tool used for decision support and resource allocation. Through practical quantitative measures, such as travel time measurements and calculating coverage area dimensions, this research specifically examines how GISs can provide data modeling, tracking, predictive analysis, and visualizations. This type of analysis can then be used for policy development and decision support in areas such as the resource allocation of fire apparatus and fire station placement. In addition, this research analyzes how GIS can be incorporated into strategic planning and budget analysis.Civilian, Dallas Fire DepartmentApproved for public release. Distribution is unlimited

Pedestrian Mobility in Denver: A Mixed Methods Approach

This research is rooted in the bigger issues of climate change, urban sustainability, and the drive to make Denver more pedestrian centered despite sprawled conditions. More specifically, this research is driven by (1) the need for a holistic, multi-dimensional, and mixed geographic perspective of pedestrian mobility, (2) the lack of qualitative data regarding pedestrian mobility and (3) a need for a better understanding of the feedback between physical and perceived space and how this influences walking behavior. Given these motivations, I deploy a multidimensional framework for assessing pedestrian mobility in Denver’s Transit Oriented Development (TOD) sites, whereby there are two primary dimensions to pedestrian mobility—the spatial and the behavioral. In order to model and explore these dimensions, this research takes a mixed methods GIS approach to capture physical and perceived space, as well as actual walking behavior. To do so, 3D walk scores and walksheds were computed for TOD study sites, using conventional GIS methods, and were compared to more qualitative GIS sketch map and survey data collected on perceived space and walking behavior. The results of the mixed methods research confirm that the relationship between space and behavior is complex, whereby physical space influences perception and perception greatly influences walking behavior. Therefore, given these findings, planners need to focus efforts toward positively influencing perceptions of pedestrian space in order to effectively encourage pedestrian mobility in Denver’s auto-dominated landscape

Urban metabolism and land use modeling for urban designers and planners: A land use model for the Integrated Urban Metabolism Analysis Tool

Predicting the resource consumption in the built environment and its associated environmental consequences (urban metabolism analysis) is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. There is a critical need for a single integrated framework to analyze the consequences of urban growth and eventually predict the impacts of sustainable policies on the urbanscape. This dissertation presents the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) – an analytical framework that simulates urban metabolism by integrating urban subsystems in a single comprehensive computational environment. It reviews the existing literature on urban sustainability, urban metabolism, as well as introducing the general framework for IUMAT. IUMAT uses three separate models for quantifying environmental impacts of land-use transition, consumption of resources, and transportation. This work outlines the development of IUMAT Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict land use change patterns. By using Density-Based Spatial Clustering and normal equations, this dissertation introduces a method for generating building-form variables from Light Detection and Ranging (LIDAR) data, which can be used as a new determinant factor in land-use change modeling. The proposed Land-use Model, within IUMAT or other analytical models, can be useful to local planning officials in understanding the complexity of land-use change and developing enhanced land-use policies