Presentation of the best of papers submitted to the Committee on Geographic Information Science (AED40) for review.
A Comparison of Travel-Time Distance Calculation Versus Euclidean Distance Calculations as Applied in The Study of Food Deserts
Isabel Gutierrez, North Carolina Central UniversityShow Abstract
Timothy Mulrooney, North Carolina Central University
Through the use of GIS (Geographic Information Systems) one can map food deserts using Euclidean distance or drive-time. Euclidian Distance measures the straight-line distance from the point A to point B and is relatively simple and quick to compute using the distance formula. Drive-time measures how long it takes to get from point A to point B taking into account stops and turns using vehicular transportation. Drive-time measurements require more calculations and data. Drive-time measurements require conversion of GIS data to a network model, as well as attributes that account for speed limits, drive-time and network distance. If both of these measures model proximity to food and food deserts the same way, it reduces the need for these more resource-intensive network calculations. The purpose of this project is to see what extent there is a difference between measures of Euclidian Distance and drive-time in food security analysis since one is more resource-intensive than the other. In order to successfully conduct this research, there will be an assessment of both methods using data collected from Guilford County, North Carolina. There will also be maps and tables created using geostatistics, network calculations, z-score analysis, and the use of the Near function to compare the two.
Understanding Access to Grocery Stores: A Data-Driven Food Desert Metric Using CHAID Decision Tree Analysis
Celeste Chavis, Morgan State UniversityShow Abstract
Istiak Bhuyan, Morgan State University
A food desert is a geographic area lacking spatial and socioeconomic access to healthy foods. Disparities in food access and availability hinder public health and individual wellbeing. Access to healthy food has been evaluated by many agencies and researches yet methodically they do not comparably demarcate food deserts or identify vulnerable populations. Existing literature suggests a link between food deserts and income level and vehicle ownership. This study evaluated existing methods and proposes a novel data driven method to identify food deserts in Baltimore, Maryland. This study evaluates responses from 573 respondents for in-depth analyses for individual grocery store choice and travel decisions. Chi-Square Automatic Interaction Detector (CHAID) decision trees are used to develop a user-generated food desert metric. Income level was found as a key indicator for food desert demarcation over vehicle ownership. Network distance was applied to develop the prioritization matrix deemed a food desert. This research provided a replicable method for determining food insecure areas in a locality by aggregating individual data to identify them. Such a metric can aid policymakers in investment decisions and direct resources to areas of need.
Interaction between development intensity: An evaluation of alternative spatial weight matrices
Manman Li (email@example.com), Southeast UniversityShow Abstract
Mengying Cui, The University of Sydney
David Levinson, The University of Sydney
This paper investigates the spatial dependency of job and worker density using census block level data from 2002 to 2017 for the Minneapolis - St. Paul (Twin Cities) metropolitan area. A spatial weight matrix is proposed to express the relationship between job density and worker density for each block pair. It reveals the spatial dependency between blocks and detects their competitive and complementary nature. Through a series of regression analyses and comparisons with an adjacency matrix and an accessibility matrix, the superior performance of the correlation matrix is demonstrated.
High-Density Mobile LiDAR for Measuring Streetscape Features
Yaneev Golombek, University of Colorado, DenverShow Abstract
Wesley Marshall, University of Colorado, Denver
This study investigates the feasibility of utilizing high density mobile Light Detection and Ranging (LiDAR) to measure streetscape features that are not detectable with publicly-available aerial LiDAR. Our previous studies investigated the limits of utilizing USGS QL2 and QL1 data for streetscape extraction, utilizing voxels to measure feature location in a 3D space; the studies found that smaller features – such as landscape furniture, traffic signage, and traffic signals – were not thoroughly represented. This paper’s results suggest that mobile LiDAR’s density allows for much smaller voxels and to thoroughly measure smaller streetscape features in 3D. This includes street trees, light/lampposts, street furniture, traffic and commercial signage, building window proportions, awnings, and enclosed courtyard restaurants. Moreover, mobile LiDAR facilitated the ability to objectively measure and categorize these streetscape features in walkable, downtown-like streetscapes. The ability to compartmentalize such streetscapes into smaller cubic feet voxels to be quantitatively measured and categorized could supplement or replace conventional audit-based streetscape measurement. This study introduces new methods – based on voxel data analysis – to compile objective descriptive statistics of streetscape features and how they can be represented in 3D.
Automatic Horizontal Curve Identification for Large Areas from GIS Roadway Centerlines
Ilir Bejleri, University of FloridaShow Abstract
Xingjing Xu, University of Florida
Daniel Brown, University of Florida
Sivaramakrishnan Srinivasan, University of Florida
Nithin Agarwal, University of Florida
The geography of horizontal roadway curves is critical to various disciplines, especially to transportation safety, due to their strong correlation with traffic crashes. Remarkably, conventional GIS roadway centerlines, while fundamental and ubiquitous in current geospatial databases, do not include curve inventories. This study presents an improved method for automatic horizontal curve identification using GIS roadway centerline networks as the data source. Analyzing each vertex of the network geometry, this method identifies the curves by detecting deflections from straight lines using a vertex deflection angle threshold. Different from literature that uses a static threshold, this method develops and applies a dynamic threshold by considering two variables - the roadway speed and the centerline vertex density. The method is capable of self-adjusting using k-means clustering to compensate for uneven centerline digitization. The method can also detect spiral transitions and can handle the complexities of street networks represented using dual centerlines. The testing and validation of the method were performed on a large dataset by applying a combination of goodness of fit metric and visual inspection. The results show that this method improves curve identification accuracy and can provide broader applicability for curve identification using GIS centerlines of various representations and digitization quality in large geographic areas.
Street Network Models and Indicators for Every Urban Area in the World
Geoff Boeing, University of Southern CaliforniaShow Abstract
Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled using the open-source OSMnx software and OpenStreetMap. In total, this study models over 150 million street network nodes and over 300 million edges across 9,000 urban areas in 178 countries. This paper demonstrates the study's computational workflow, introduces two new open data repositories of processed global street network models and calculated indicators, and discusses analytical findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of using this software tool in spatial network modeling and analyses with open big data. Second, it produces an open data repository containing models for each of these urban areas, in various file formats, for public reuse. Third, it produces an open data repository containing dozens of calculated indicators of street network and urban form for each urban area. No such global urban street network indicator data set has previously existed. Fourth, it presents a preliminary descriptive spatial/network analysis of worldwide street network form at the metropolitan scale, reporting the first such analytical results in the literature.
A Comparison of Roadway Grade Estimations from GPS and Barometer Measurements
Michael Pratt, Texas A&M UniversityShow Abstract
Raul Avelar, Texas A&M Transportation Institute
Roadway grade is used as an input variable for several highway analysis tasks, including evaluating curve margin of safety, applying safety prediction models, assessing the adequacy of sight distance, and maintaining state roadlog databases. However, collecting accurate grade data is often expensive due to the need to obtain field measurements or review as-built plan sets. Agencies would benefit from the development of a method to compute roadway grade from an automated data collection system. This paper documents a comparison of roadway grade estimations computed from global positioning system (GPS) and barometric altimeter data streams obtained during test drives on highway segments of interest. To calibrate the estimation method, the authors obtained ground-truth grade measurements collected from the field and then modeled those measures as response in a time-series model with the two data stream types as explanatory variables. The authors found that for many applications, the elevation data obtained from GPS is adequate to obtain reasonable estimates, but such estimations can be improved with a supplemental data stream from a barometer.
Empirical Estimation of Route Length Along U.S. Interstate Highways Based on Euclidean Distance
Nawei Liu, University of TennesseeShow Abstract
Fei Xie, Oak Ridge National Laboratory
Zhenhong Lin (firstname.lastname@example.org), Oak Ridge National Laboratory
Mingzhou Jin, University of Tennessee
In this study, we specified 98 regression models for easily estimating shortest distances based on Euclidean distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance or even distance matrix without spending significant efforts in complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the Euclidean distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), we considered two different models, with and without the intercept. Based on the adjusted R-squared, we observed that models without intercepts have generally better fitness. All these models generally have good fitness with the linear regression relationship between the Euclidean distance and route distance. At the state level, we also observe significant variations in the slope coefficients between state-level models. We further conducted a preliminary analysis of the impact of highway density on this variation.
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