Integrating GIS into Food Access Analysis

Daoqin Tong is an associate professor of Geography and Development at the University of Arizona. This presentation was given as part of the GIS Day@KU symposium on November 18, 2015. For more information about GIS Day@KU activities, please see http://www.gis.ku.edu/gisday/2015/.Platinum Sponsors: KU Department of Geography and Atmospheric Science; KU School of Business. Gold Sponsors: Bartlett & West; Kansas Biological Survey; KU Environmental Studies Program; KU Institute for Policy & Social Research; KU Libraries. Silver Sponsors: State of Kansas Data Access and Support Center (DASC). Bronze Sponsors: KU Center for Remote Sensing of Ice Sheets (CReSIS); TREKK Design Group, LLC; Wilson & Company, Engineers and Architects

SPATIAL DISPARITIES IN THE CHINESE ICT SECTOR: A REGIONAL ANALYSIS

The information and communication technology (ICT) sector is currently one of the most dynamic sectors in China’s economy. Based on the number of cell phone users, internet users and workers in telecommunication, we indicate that the ICT sector is not equally distributed across the 31 Chinese provinces. This is also true for the distribution of per capita income growth. Various tools of exploratory spatial data analysis are then used to uncover that this sector displays signs of spatial autocorrelation as the selected variables appear to be more spatially concentrated in a few provinces. However, while cell phones and internet are mostly clustered in the East, workers in telecommunication are relatively more abundant in the Northern part of the country. On the other hand, the provincial growth rate is more randomly distributed. The existence of a positive relation between the number of ICT users in one province and growth in the neighbouring provinces suggests that ICT ought to be considered as one of the potential levers of a policy aiming to reduce regional inequalities.INFORMATION AND COMMUNICATION TECHNOLOGIES,

Consumer Behavior Choice in the Era of Shared Mobility: The Role of Proximity, Competition, and Quality

Shared mobility services, which allow users to make point-to-point trips on an as-needed basis, have drastically impacted people’s travel behavior in the last few years. In this study, we propose a decision choice model to examine the factors that influence the restaurant choice of individuals who use shared mobility services. Our model incorporates key elements from the spatial interaction model and the theory of the individual decision making from economics. We analyze individuals’ travel behavior using trip-level data, along with point of interest data, restaurant reviews and average prices, and travel route characteristics. We find that the effect of proximity of a restaurant depends on the total distance of the trip. For shorter trips, an individual is less likely to choose a restaurant that is further away. However, if an individual decides to travel a long distance to a restaurant, she is more likely to choose a restaurant that is further. Additionally, with increasing travel distance (or competition) there is a decreased preference for a restaurant with a higher price. The quality (online reviews) of a restaurant does not seem to have a significant impact on the choice of the restaurant. Implications of the study are discussed

Urban Transportation System Flood Vulnerability Assessment with Special Reference to Low Income and Minority Neighborhoods

A flood vulnerability assessment of the City of Tucson, Arizona’s transportation systems was conducted with special reference to low-income and minority neighborhoods. Short-term flooding from extreme storm events pose a serious challenge to transportation system reliability and emergency response in cities across the United States. This problem, which is anticipated to grow over the next century due to climate change, is often hardest on vulnerable populations, including low-income and minority neighborhoods. Our work aimed to advance national research methods for assessing multi-modal transportation degradation due to flooding. We identified priority locations for Tucson to make transportation improvement investments for the purpose of mitigating urban transportation system flooding. This included increasing equitable accessibility to the multi-modal transportation network across three modes: vehicular, bicycle, and public transportation via pedestrian access to bus stops. As a case study, our proposal has national flood hazard transportation vulnerability and equity implications. The project had three stages. In Stage 1 we estimated flood conditions based on a 5-year, 1-hour storm event with FLO-2D and a digital elevation model (DEM) constructed using LiDAR data. In Stage 2 we analyzed neighborhood transportation vulnerability based on overall transportation system performance and use across the three transportation networks. In Stage 3, we performed thirty (the top ten sites for the three modes of transportation) green infrastructure (GI) scenario analyses to determine the impact that GI implementations could have on the multimodal system. Of the thirty areas studied, 93% were part of census tracts with median household incomes below the Tucson average. We found that GI implementation performs most effectively to increase multi-modal access when implemented in moderate flooding conditions. In extreme cases, comprehensive, neighborhood-scale GI implementation did not result in creating greater accessibility during flood events. Rather than municipalities selecting areas for GI implementation that have the highest volumes of flooding or citizen complaints, GI implementation funds may be invested in moderate flooded area for greatest improvement of multimodal access. Future research will assess impact across time durations (rather than simple peak event calculations) and work to optimize GI implementation across multiple benefits for multiple modes of transportation (rather than individual modes). We plan to communicate our findings broadly. This research is a proof of concept for a larger, long-term project to advance national research methods to reduce the impact of chronic flooding on the multi-modal transportation network. Additional funding from NITC and other sources is currently being targeted

The Potential of Green Infrastructure in Mitigating Flood Impacts on the Mobility of Low Income and Minority Neighborhoods

Short-term flooding from extreme storm events poses a serious transportation challenge in U.S. cities. This problem—which is anticipated to grow over the next century with our global climate crisis—is often hardest on vulnerable populations, including low-income and minority neighborhoods. This project advances national research methods for assessing flood vulnerability and prioritizing transportation improvement investments to ensure that no community is left stranded when the next flood occurs

Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges

In the past few years, station-free bike sharing systems (SFBSSs) have been adopted in many cities worldwide. Different from conventional station-based bike sharing systems (SBBSSs) that rely upon fixed bike stations, SFBSSs allow users the flexibility to locate a bike nearby and park it at any appropriate site after use. With no fixed bike stations, the spatial extent/scale used to evaluate bike shortage/surplus in an SFBSS has been rather arbitrary in existing studies. On the one hand, a balanced status using large areas may contain multiple local bike shortage/surplus sites, leading to a less effective rebalancing design. On the other hand, an imbalance evaluation conducted in small areas may not be meaningful or necessary, while significantly increasing the computational complexity. In this study, we examine the impacts of analysis scale on the SFBSS imbalance evaluation and the associated rebalancing design. In particular, we develop a spatial optimization model to strategically optimize bike rebalancing in an SFBSS. We also propose a region decomposition method to solve large-sized bike rebalancing problems that are constructed based on fine analysis scales. We apply the approach to study the SFBSS in downtown Beijing. The empirical study shows that imbalance evaluation results and optimal rebalancing design can vary substantially with analysis scale. According to the optimal rebalancing results, bike repositioning tends to take place among neighboring areas. Based on the empirical study, we would recommend 800 m and 100/200 m as the suitable scale for designing operator-based and user-based rebalancing plans, respectively. Computational results show that the region decomposition method can be used to solve problems that cannot be handled by existing commercial optimization software. This study provides important insights into effective bike-share rebalancing strategies and urban bike transportation planning

Maximizing Wireless Mesh Network Coverage

As an emerging technology, wireless communication revolutionizes the way data are shared and transferred. In particular, wireless mesh network (WMN) technology allows data transmission from one node to another without extensive cabling. In this article, spatial characteristics of maximal covering problems are explored, and a novel spatial optimization model is proposed for WMN topology planning. The model selects the optimal locations for network infrastructure to achieve the maximal coverage of spatial demand. Additionally, important WMN design requirements have been accounted for, including network topology and throughput capacity. The validity of the model is tested through a WMN deployment developed for an emergency medical service application in Tucson, Arizona.wireless mesh network; network topology planning; maximal coverage; spatial optimization

Development of a temporal and spatial linkage between transit demand and landuse patterns

We are exploring ways to capture the temporal and spatial dimensions of the use of public transit. Specifically, we are investigating how different land uses affect the spatial and temporal demand for public transit services. Spatially, the availability of new data collection technology in public transit allows us to examine transit demand at the individual stop level. Our hypothesis, however, is that transit users' activity may not be originated from or destined to an individual stop per se; rather, the activity is associated with a specific location in the vicinity of the stop, and this location may be "covered" by several adjacent transit stops. More importantly, understanding the transit demand at this aggregate level (an aggregate "catchment" area) can enhance the ability to define a specific land-use type and the temporal characteristics related to passengers' activities. Temporally, we seek to understand the relationship between the demand for public transit service at specific times of the day and the associated land uses that may strongly influence the timing of that demand. To explore these dimensions, this study: 1) proposes a method of stop aggregation; 2) generates transit service areas based on these aggregated stops; 3) develops a set of metrics to better represent land-use types within these service areas; and 4) examines the spatial and temporal characteristics of transit demand for these service areas. These methods are applied to a case study using land-use and transit demand data from the Minneapolis-St. Paul metropolitan area