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Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas.
Lan Mu; Yusi Liu; Donglan Zhang; Yong Gao; Michelle Nuss; Janani Rajbhandari-Thapa; Zhuo Chen; José Pagán; Yan Li; Gang Li; Heejung Son. Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States. ISPRS International Journal of Geo-Information 2021, 10, 417 .
AMA StyleLan Mu, Yusi Liu, Donglan Zhang, Yong Gao, Michelle Nuss, Janani Rajbhandari-Thapa, Zhuo Chen, José Pagán, Yan Li, Gang Li, Heejung Son. Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States. ISPRS International Journal of Geo-Information. 2021; 10 (6):417.
Chicago/Turabian StyleLan Mu; Yusi Liu; Donglan Zhang; Yong Gao; Michelle Nuss; Janani Rajbhandari-Thapa; Zhuo Chen; José Pagán; Yan Li; Gang Li; Heejung Son. 2021. "Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States." ISPRS International Journal of Geo-Information 10, no. 6: 417.
Older adult migration patterns are shaped by distinct sets of factors for intrastate versus interstate and younger (65–74) versus aged (75+) older migrants. Moving decisions relate to individual factors and the destination's characteristics, including the climate, amenity, and cost of living. Previous studies have rarely integrated destination characteristics such as long-term care (LTC) facilities, affordable housing, or geriatricians. This study addresses this gap by examining older adult migration by age group and migration type. We proposed a variable structure describing six living environment categories of destination counties and further investigated the relationship between migrants and destination characteristics. We identified geographical patterns of migration and used linear regression and decision tree models to analyze migrants' destination variables. Results indicated four subgroups of older migrants have different high-high clusters in or near the Atlanta region and low-low clusters in South Georgia. Linear regression models quantified the relationship and indicated variables such as LTC facility bed and affordable housing availability should be considered in older adult migration analysis. Decision tree models revealed that different variables are associated with certain county groups, such as core Atlanta and rural counties. Our findings highlighted the variety of variables shaping the migration of older adult subgroups.
Xuan Zhang; Lan Mu; Jerry Shannon. The relationship between older adult migration and destination characteristics in Georgia. Applied Geography 2021, 132, 102464 .
AMA StyleXuan Zhang, Lan Mu, Jerry Shannon. The relationship between older adult migration and destination characteristics in Georgia. Applied Geography. 2021; 132 ():102464.
Chicago/Turabian StyleXuan Zhang; Lan Mu; Jerry Shannon. 2021. "The relationship between older adult migration and destination characteristics in Georgia." Applied Geography 132, no. : 102464.
In December 2019, the coronavirus disease 2019 (COVID-19) pandemic attacked Wuhan, China. The city government soon strictly locked down the city, implemented a hierarchical diagnosis and treatment system, and took a series of unprecedented pharmaceutical and non-pharmaceutical measures. The residents’ access to the medical resources and the consequently potential demand–supply tension may determine effective diagnosis and treatment, for which travel distance and time are key indicators. Using the Application Programming Interface (API) of Baidu Map, we estimated the travel distance and time from communities to the medical facilities capable of treating COVID-19 patients, and we identified the service areas of those facilities as well. The results showed significant differences in service areas and potential loading across medical facilities. The accessibility of medical facilities in the peripheral areas was inferior to those in the central areas; there was spatial inequality of medical resources within and across districts; the amount of community healthcare centers was insufficient; some communities were underserved regarding walking distance; some medical facilities could be potentially overloaded. This study provides reference, in the context of Wuhan, for understanding the spatial aspect of medical resources and residents’ relevant mobility under the emergency regulation, and re-examining the coordination of emergency to improve future planning and utilization of medical facilities at various levels. The approach can facilitate policymakers to assess potential loading of medical facilities, identify low-accessibility areas, and deploy new medical facilities. It also implies that the accessibility analysis can be rapid and relevant even only with open-source data.
Zhenqi Zhou; Zhen Xu; Anqi Liu; Shuang Zhou; Lan Mu; Xuan Zhang. Mapping the Accessibility of Medical Facilities of Wuhan during the COVID-19 Pandemic. ISPRS International Journal of Geo-Information 2021, 10, 318 .
AMA StyleZhenqi Zhou, Zhen Xu, Anqi Liu, Shuang Zhou, Lan Mu, Xuan Zhang. Mapping the Accessibility of Medical Facilities of Wuhan during the COVID-19 Pandemic. ISPRS International Journal of Geo-Information. 2021; 10 (5):318.
Chicago/Turabian StyleZhenqi Zhou; Zhen Xu; Anqi Liu; Shuang Zhou; Lan Mu; Xuan Zhang. 2021. "Mapping the Accessibility of Medical Facilities of Wuhan during the COVID-19 Pandemic." ISPRS International Journal of Geo-Information 10, no. 5: 318.
In the context of rapid development, Beijing, the capital of China, is facing huge challenges in providing fair healthcare resources to residents. Although Beijing has the best healthcare resources nationwide, a highly concentrated population and uneven distribution of hospitals make the supply of medical resources tight and unbalanced. The objective of this study is to explore the healthcare resource inequality in Beijing based on spatial accessibility. The two‐step floating catchment area method was improved to measure healthcare accessibility by defining a novel distance attenuation function that conforms to the specific travel behavior of taxies to hospitals. We explored the inequality among different places and different populations. It was found that the spatial inequality of healthcare resources was evident and typical, with the dominant resources concentrated in the city center. Some regions are always in an advantageous position regardless of traffic conditions. The impact of some social‐economic factors on healthcare accessibility was analyzed, which exhibited significant spatial heterogeneity. Hospital deserts for different vulnerable populations were identified. Besides children with massive hospital deserts at the city fringe, other vulnerable populations have no distinct disadvantage. These results offer profound comprehension of healthcare inequality to assist in healthcare resources management and policy‐making in Beijing.
Shize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS 2021, 25, 1504 -1521.
AMA StyleShize Gong, Yong Gao, Fan Zhang, Lan Mu, Chaogui Kang, Yu Liu. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS. 2021; 25 (3):1504-1521.
Chicago/Turabian StyleShize Gong; Yong Gao; Fan Zhang; Lan Mu; Chaogui Kang; Yu Liu. 2021. "Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement." Transactions in GIS 25, no. 3: 1504-1521.
While several studies have explored geographic relationships within the Supplemental Nutrition Assistance Program (SNAP), results have been mixed. Findings have revealed an imbalanced SNAP participation rate among eligible populations in both suburban and rural areas. Studies on SNAP accessibility have often focused on store locations, but few have examined issues of traveling to SNAP offices. In this study, we focused on SNAP office accessibility and its association with rurality. Using Google Map API, we calculated road travel time and distance to the most conveniently located SNAP office for each block group in the conterminous United States. We investigated the degree to which the SNAP office accessibility is linked to rurality, participant demographics, socioeconomic characteristics, and the program's overall participation rate. Results showed that at the block-group level, the SNAP-eligible average driving time to the most convenient SNAP office is 15.28 min, while the SNAP-eligible average distance is 8.57 miles. More than 75% of the SNAP-income eligible population lives within a 20-min drive to a SNAP office, and 91.8% lives within 30 min. Every ten percentage points of increase in rurality decreases SNAP office accessibility by one additional minute of car travel time. We designed a Rurality-Travel Clock (RTC) visualization tool to provide a graphic description of the urban-rural setting and SNAP office accessibility. Other observations include a noticeable cross-relationship between population concentration (e.g., white or black) and SNAP office accessibility. The findings help us to understand the dynamic relationships between SNAP participation rate and SNAP accessibility factors, including eligibility, employment status, population, and rurality at the county and state levels.
Lan Mu; Yu Chen; Chen Zhen. SNAP office accessibility and its association with rurality. Applied Geography 2020, 120, 102209 .
AMA StyleLan Mu, Yu Chen, Chen Zhen. SNAP office accessibility and its association with rurality. Applied Geography. 2020; 120 ():102209.
Chicago/Turabian StyleLan Mu; Yu Chen; Chen Zhen. 2020. "SNAP office accessibility and its association with rurality." Applied Geography 120, no. : 102209.
The prosperity of ride-sharing services has rippled in the communities of GIScience, transportation, and urban planning. Meanwhile, road network structure has been analyzed from a network science perspective that focuses on nodes and relational links and aims to predictive models. However, limited empirical studies have explored the relationship between road network structure and ride-sharing accessibility through such perspective. This paper utilizes the spatial Durbin model to understand the relationship between road network structure and ride-sharing accessibility, proxied by Uber accessibility, through classical network measures of degree, closeness, and betweenness centrality. Taking the city of Atlanta as a case study, we have found in addition to population density and road network density, larger values of degree centrality and smaller values of closeness centrality of the road network are associated with better accessibility of Uber services. However, the effects of betweenness centrality are not significant. Furthermore, we have revealed heterogeneous effects of degree centrality and closeness centrality on the accessibility of Uber services, as the magnitudes of their effects vary by different time windows (i.e., weekday vs. weekend, rush hour in the morning vs. evening). Network science provides us both conceptual and methodological measures to understand the association between road network structure and ride-sharing accessibility. In this study, we constructed road network structure measures with OpenStreetMap, which is reproducible, replicable, and scalable because of its global coverage and public availability. The study resonates with the notion of cities as the set of interactions across networks, as we have observed time-sensitive heterogeneous effects of road network structure on ride-sharing accessibility.
Mingshu Wang; Zheyan Chen; Lan Mu; Xuan Zhang. Road network structure and ride-sharing accessibility: A network science perspective. Computers, Environment and Urban Systems 2019, 80, 101430 .
AMA StyleMingshu Wang, Zheyan Chen, Lan Mu, Xuan Zhang. Road network structure and ride-sharing accessibility: A network science perspective. Computers, Environment and Urban Systems. 2019; 80 ():101430.
Chicago/Turabian StyleMingshu Wang; Zheyan Chen; Lan Mu; Xuan Zhang. 2019. "Road network structure and ride-sharing accessibility: A network science perspective." Computers, Environment and Urban Systems 80, no. : 101430.
Existing walkability measurements have not considered some important components of the built environment, pedestrians’ preferences, or all walking purposes. As area-based measurements, they may overlook some detailed walkability changes. We propose a Perceived importance and Objective measure of Walkability in the built Environment Rating (POWER) method, which is a line-based approach considering both the perception of pedestrians and subjective characterizing of the urban built environment. Incorporating online survey and social media data, we present a built environment walkability study in a specific environment and the potential for more general scenarios. The survey can be customized for the particular urban environment and capture the preferences of a local population. The social media obtain general opinions from a broader audience. Although focusing on the specific setting at a university campus, we also included the general social media results to supplement the POWER structure and survey findings. Using social media and survey results can bring two scales together to provide a more complete understanding of walkability.
Xuan Zhang; Lan Mu. Incorporating Online Survey and Social Media Data into a GIS Analysis for Measuring Walkability. Ecology of Tuberculosis in India 2019, 133 -155.
AMA StyleXuan Zhang, Lan Mu. Incorporating Online Survey and Social Media Data into a GIS Analysis for Measuring Walkability. Ecology of Tuberculosis in India. 2019; ():133-155.
Chicago/Turabian StyleXuan Zhang; Lan Mu. 2019. "Incorporating Online Survey and Social Media Data into a GIS Analysis for Measuring Walkability." Ecology of Tuberculosis in India , no. : 133-155.
Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.
Yong Gao; Jiajun Liu; Yan Xu; Lan Mu; Yu Liu. A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips. Sustainability 2019, 11, 4214 .
AMA StyleYong Gao, Jiajun Liu, Yan Xu, Lan Mu, Yu Liu. A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips. Sustainability. 2019; 11 (15):4214.
Chicago/Turabian StyleYong Gao; Jiajun Liu; Yan Xu; Lan Mu; Yu Liu. 2019. "A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips." Sustainability 11, no. 15: 4214.
Lan Mu; Steven Holloway; University Of Georgia. Neighborhoods. Geographic Information Science & Technology Body of Knowledge 2019, 2019, 1 .
AMA StyleLan Mu, Steven Holloway, University Of Georgia. Neighborhoods. Geographic Information Science & Technology Body of Knowledge. 2019; 2019 (Q1):1.
Chicago/Turabian StyleLan Mu; Steven Holloway; University Of Georgia. 2019. "Neighborhoods." Geographic Information Science & Technology Body of Knowledge 2019, no. Q1: 1.
Location-based social media provide an enormous stream of data about humans' life and behavior. With geospatial methods, those data can offer rich insights into public health. In this research, we study the effect of climate and seasonality on the prevalence of depression in Twitter users in the U.S. Text mining and geospatial methods are used to detect tweets related to depression and their spatiotemporal patterns at the scale of Metropolitan Statistical Area. We find the relationship between depression rates, climate risk factors and seasonality are varied and geographically localized. The same climate measure may have opposite association with depression rates at different places. Relative humidity, temperature, sea level pressure, precipitation, snowfall, weed speed, globe solar radiation, and length of day all contribute to the geographic variations of depression rates. A conceptual compact map is designed to visualize scattered geographic phenomena in a large area. We also propose a three-stage framework that semi-automatically detects and analyzes geographically distributed health issues using location-based social media data.
Wei Yang; Lan Mu; Ye Shen. Effect of climate and seasonality on depressed mood among twitter users. Applied Geography 2015, 63, 184 -191.
AMA StyleWei Yang, Lan Mu, Ye Shen. Effect of climate and seasonality on depressed mood among twitter users. Applied Geography. 2015; 63 ():184-191.
Chicago/Turabian StyleWei Yang; Lan Mu; Ye Shen. 2015. "Effect of climate and seasonality on depressed mood among twitter users." Applied Geography 63, no. : 184-191.
Spatial demand representation is critical for applying location models to planning processes and the efficiency of modeling solutions. Current research has focused primarily on assessing and mitigating demand representation error but ignored the computational complexity of implementing demand representations and solving the associated models. We first use set theory to formulize Cromley et al's (Institutional Journal of Geographical Information Science 26 495-512) demand representation with the least common demand coverage unit (LCDCU). Then, in the application of using the maximal covering location problem (MCLP) to site base stations optimally for a cellular network, we compare the LCDCU-based representation with widely used point-lattice-based and polygon-lattice-based demand representations in terms of both computational complexity and representation error. The LCDCU-based representation creates demand objects by partitioning a demand space into potential service areas, and has several advantages including offering solutions that provide real 100% demand coverage and eliminating some errors associated with other demand representations. However, the computational complexity of implementing the LCDCU-based representation could easily become extremely high as the number of potential facility sites increases, which could be a challenge to current geographic information systems. In addition, unlike point-based and polygon-based demand representations, the LCDCU-based representations cannot be applied to the planar covering location problems where a facility can be sited anywhere. The results of our study suggest that point-lattice-based demand representations with fine granularity are a good alternative to the LCDCU-based representations due to their effective modeling solutions without extensive computation. Polygon-lattice-based demand representations are not recommended owing to both high computational complexity and relatively large representation error. This study provides some indicators on how to choose an appropriate spatial demand representation in practical applications.
Ping Yin; Lan Mu. An empirical comparison of spatial demand representations in maximal coverage modeling. Environment and Planning B: Planning and Design 2015, 42, 574 -592.
AMA StylePing Yin, Lan Mu. An empirical comparison of spatial demand representations in maximal coverage modeling. Environment and Planning B: Planning and Design. 2015; 42 (4):574-592.
Chicago/Turabian StylePing Yin; Lan Mu. 2015. "An empirical comparison of spatial demand representations in maximal coverage modeling." Environment and Planning B: Planning and Design 42, no. 4: 574-592.
Similar geographic areas often have great variations in population size. In health data management and analysis, it is desirable to obtain regions of comparable population by decomposing areas of large population (to gain more spatial variability) and merging areas of small population (to mask privacy of data). Based on the Peano curve algorithm and modified scale-space clustering, this research proposes a mixed-level regionalization (MLR) method to construct geographic areas with comparable population. The method accounts for spatial connectivity and compactness, attributive homogeneity, and exogenous criteria such as minimum (and approximately equal) population or disease counts. A case study using Louisiana cancer data illustrates the MLR method and its strengths and limitations. A major benefit of the method is that most upper level geographic boundaries can be preserved to increase familiarity of constructed areas. Therefore, the MLR method is more human-oriented and place-based than computer-oriented and space-based.
Lan Mu; Fahui Wang; Vivien W. Chen; Xiao-Cheng Wu. A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis. Annals of the Association of American Geographers 2014, 105, 48 -66.
AMA StyleLan Mu, Fahui Wang, Vivien W. Chen, Xiao-Cheng Wu. A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis. Annals of the Association of American Geographers. 2014; 105 (1):48-66.
Chicago/Turabian StyleLan Mu; Fahui Wang; Vivien W. Chen; Xiao-Cheng Wu. 2014. "A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis." Annals of the Association of American Geographers 105, no. 1: 48-66.
Depression is a common chronic disorder. It often goes undetected due to limited diagnosis methods and brings serious results to public and personal health. Former research detected geographic pattern for depression using questionnaires or self-reported measures of mental health, this may induce same-source bias. Recent studies use social media for depression detection but none of them examines the geographic patterns. In this paper, we apply GIS methods to social media data to provide new perspectives for public health research. We design a procedure to automatically detect depressed users in Twitter and analyze their spatial patterns using GIS technology. This method can improve diagnosis techniques for depression. It is faster at collecting data and more promptly at analyzing and providing results. Also, this method can be expanded to detect other major events in real-time, such as disease outbreaks and earthquakes.
Wei Yang; Lan Mu. GIS analysis of depression among Twitter users. Applied Geography 2014, 60, 217 -223.
AMA StyleWei Yang, Lan Mu. GIS analysis of depression among Twitter users. Applied Geography. 2014; 60 ():217-223.
Chicago/Turabian StyleWei Yang; Lan Mu. 2014. "GIS analysis of depression among Twitter users." Applied Geography 60, no. : 217-223.
Ping Yin; Lan Mu. Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles. Applied Geography 2012, 34, 247 -254.
AMA StylePing Yin, Lan Mu. Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles. Applied Geography. 2012; 34 ():247-254.
Chicago/Turabian StylePing Yin; Lan Mu. 2012. "Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles." Applied Geography 34, no. : 247-254.