Yongwan Chun
Datos Biográficos
| ID | 3635591 |
|---|---|
| NOMBRE | Yongwan Chun |
| NOMBRES | Yongwan |
| APELLIDO | Chun |
| FIRMA | CHUN Y |
| AFILIACIONES | The University of Texas at Dallas |
| ORCID | 0000-0002-4957-1379 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 27 |
| TOTAL DE CITAS | 25 |
| TOTAL COMO AUTOR | 27 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2009 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 3 |
A Machine Learning Approach Using Spatially Explicit K-Nearest Neighbors for House Price Predictions
Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit consideration. If these inherent spatial structures are ignored, models may produce biased predictions. However, integrating this property into the model yields additional spatial insight, thereby en…
An Efficient Solution Approach for the p -Median Problems with Spatially Autocorrelated Weights
The p-median problem (PMP) is a classical location-allocation problem that involves simultaneously determining the locations of facilities and allocating demand points (nonfacilities) in a discrete space. The PMP is known to be NP-hard. Given its wide applicability, obtaining optimal solutions for large instances, particularly when multiple optimal solutions exist, remains computationally challenging. Nevertheless, there is a continuing and consi…
A Spatially Informed Solving Approach for the Traveling Salesman Problem
The traveling salesman problem (TSP) is a combinatorial optimization problem that seeks to determine the optimal route that minimizes the travel cost among a given set of nodes. Because solving the TSP inherently requires an exhaustive search, examining all possible routes to achieve the optimal solution, it becomes computationally challenging, particularly with an increase of problem size. Ensuring optimality while making the problem effectively…
An Efficient Approach for Solving Hub Location Problems Using Network Autocorrelation Structures
The properties of spatial information have been shown to aid in identifying optimal solutions for location–allocation problems. Little effort, though, has been made to develop a spatially informed approach to solving hub location problems, as this class of problems entails a more complex model structure and greater challenges in terms of solving capability. To address this issue, this research proposes the spatially informed hub location problem …
Delineations for Police Patrolling on Street Network Segments with p-Median Location Models
Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This pap…
An Efficient Solving Approach for the p ‐Dispersion Problem Based on the Distance‐Based Spatially Informed Property
The p ‐dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named the distance‐based spatially informed property (D‐SIP) method to reduce the problem size of the p ‐dispersion instances, facilitating a more efficient solution while mainta…
Location Planning of Emergency Medical Facilities Using the p-Dispersed-Median Modeling Approach
This research employs a spatial optimization approach customized for addressing equitable emergency medical facility location problems through the p-dispersed-median problem (p-DIME). The p-DIME integrates two conflicting classes of spatial optimization problems, dispersion and median problems, aiming to identify the optimal locations for emergency medical facilities to achieve an equitable spatial distribution of emergency medical services (EMS)…
The Majority Theorem for the Single ( p = 1) Median Problem and Local Spatial Autocorrelation
Except for about a half dozen papers, virtually all (co)authored by Griffith, the existing literature lacks much content about the interface between spatial optimization, a popular form of geographic analysis, and spatial autocorrelation, a fundamental property of georeferenced data. The popular p ‐median location‐allocation problem highlights this situation: the empirical geographic distribution of demand virtually always exhibits positive spati…
Modeling Crime Density with Population Dynamics in Space and Time
The current study explores populational and environmental factors associated with violent crime. Specifically, it compares ambient and residential populations with regard to their association with assault density at a fine spatial and temporal unit in a city with socio-economic control variables. The results show that the ambient population are consistently associated with the level of assaults throughout the four time periods in a day, while res…
Assessing Trauma Center Accessibility for Healthcare Equity Using an Anti-Covering Approach
Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient's chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current pl…
Measuring Local Spatial Autocorrelation with Data Reliability Information
Local spatial autocorrelation (SA) measures have been used in exploratory spatial data analysis, particularly in detecting spatial clusters. Existing local SA measures, however, are likely unreliable and biased because they compare only estimates among neighboring spatial units, ignoring errors associated with these estimates. The spatial Bhattacharyya coefficient (SBC) compares probability distributions by considering both estimates and their st…
Modeling Community Health with Areal Data
Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider p…
Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants
A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr), measured during its assaying; errors for these measurements are analyzed in this paper, with an objective of contributing to the geography of…
A Moran eigenvector spatial filtering specification of entropy measures
Temperature and assault in an urban environment
Deeper Spatial Statistical Insights into Small Geographic Area Data Uncertainty
Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small local sample sizes, which tend to inflate local uncertainty and undermine otherwise sound statistical analyses. This condition is the opposite of that afflicting statistical sig…
Space-time cluster detection with cross-space-time relative risk functions
Space-time kernel density estimation (STKDE) commonly is used for space-time cluster detection. But, this technique might be limited because it does not take into account an underlying population at risk for observed events. A space-time relative risk function (STRRF) can help overcome this limitation by allowing a comparison of each kernel density of observations with that of controls. This paper proposes a cross-STRRF to identify spatio-tempora…
Revisiting environmental inequity in Southern California
This study revisits the concept of environmental inequity in Southern California using the California Environmental Protection Agency’s most recent data and spatial models. Empirical studies in the late 1990s documented the existence of environmental inequity among disadvantaged populations in the area, and we still found evidence of environmental inequity. However, our findings were more nuanced and subtler than previous results. The risk of bei…
Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates
The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introd…
Measuring Spatial Dependence
Measuring spatial dependence is an essential process for diagnosing if conventional statistical approaches would produce proper results, and/or if spatial statistical methods need to be employed. It involves an operational framework in which spatial dependence is captured, numerical indices and their statistical properties that are used for a significance test, and graphics to visually portray spatial dependence. Because methods measuring spatial…
Optimal Map Classification Incorporating Uncertainty Information
A choropleth map frequently is used to portray the spatial pattern of attributes, and its mapping result heavily relies on map classification. Uncertainty in an attribute has an influence on map classification and, accordingly, can generate an unreliable spatial pattern. Only a few studies, however, have explored the implications of uncertainty in map classification. Recent studies present methods to incorporate uncertainty in map classification …
Space–Time Analysis
Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel …
Modeling Sanction Choices on Fraudulent Benefit Exchanges in Public Service Delivery
Public service delivery programs are not free from players' opportunistic behaviors, such as fraudulent benefit exchanges. The standard methods used to detect such misbehaviors are static, less effective in uncovering interactions between corrupt agents, and easy to evade because of corrupt agents' familiarity with detection procedures. Current fraud detection efforts do not match the dynamics and adaptive processes they are supposed to monitor a…
Using Bayesian Methods to Control for Spatial Autocorrelation in Environmental Justice Research
Many previous environmental justice (EJ) studies have argued that there is disproportionate collocation of environmental disamenities with racial and ethnic minorities, even holding constant other factors such as income and political action. However, most of the EJ studies do not account for the presence of spatial autocorrelation, especially those that also include nonnormal distributions. Using the location of new Toxics Release Inventory facil…
Modeling Network Autocorrelation in Space–Time Migration Flow Data
Gravity-type spatial interaction models have been popularly utilized in modeling cross-sectional migration data, but their misspecification also has been raised in the literature. This misspecification issue principally concerns an insufficient accounting of underlying effects of spatial structure, including the presence of network autocorrelation among migration flows. Recent studies reveal that spatial interaction models are significantly impro…
Modeling Network Autocorrelation in Space–Time Migration Flow Data
Gravity-type spatial interaction models have been popularly utilized in modeling cross-sectional migration data, but their misspecification also has been raised in the literature. This misspecification issue principally concerns an insufficient accounting of underlying effects of spatial structure, including the presence of network autocorrelation among migration flows. Recent studies reveal that spatial interaction models are significantly impro…
Space–Time Analysis
Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel …
Using Bayesian Methods to Control for Spatial Autocorrelation in Environmental Justice Research
Many previous environmental justice (EJ) studies have argued that there is disproportionate collocation of environmental disamenities with racial and ethnic minorities, even holding constant other factors such as income and political action. However, most of the EJ studies do not account for the presence of spatial autocorrelation, especially those that also include nonnormal distributions. Using the location of new Toxics Release Inventory facil…
Spatial Autoregressive Model for Population Estimation at the Census Block Level Using Lidar-derived Building Volume Information
The collection of population by census is laborious, time consuming and expensive, and often only available at limited temporal and spatial scales. Remote sensing based population estimation has been employed as a viable alternative for providing population estimates based on indicators that make use of two-dimensional areal information of buildings or one-dimensional length information of roads The recent advancement of LIDAR remote sensing prov…
Visualizing Migration Flows Using Kriskograms
This paper describes a new approach called kriskogram to visualizing migration flows. To create a kriskogram, geographical units are projected as a set of points on a straight line segment called a location line. The migration flow between two points on the location line is represented using a half-circle drawn from the origin to the destination in a clockwise direction. Translucent symbols and a classification scheme can be used to make a krisko…
An Efficient Approach for Solving Hub Location Problems Using Network Autocorrelation Structures
The properties of spatial information have been shown to aid in identifying optimal solutions for location–allocation problems. Little effort, though, has been made to develop a spatially informed approach to solving hub location problems, as this class of problems entails a more complex model structure and greater challenges in terms of solving capability. To address this issue, this research proposes the spatially informed hub location problem …
A Moran eigenvector spatial filtering specification of entropy measures
Optimal Map Classification Incorporating Uncertainty Information
A choropleth map frequently is used to portray the spatial pattern of attributes, and its mapping result heavily relies on map classification. Uncertainty in an attribute has an influence on map classification and, accordingly, can generate an unreliable spatial pattern. Only a few studies, however, have explored the implications of uncertainty in map classification. Recent studies present methods to incorporate uncertainty in map classification …
Visualizing Migration Flows Using Kriskograms
This paper describes a new approach called kriskogram to visualizing migration flows. To create a kriskogram, geographical units are projected as a set of points on a straight line segment called a location line. The migration flow between two points on the location line is represented using a half-circle drawn from the origin to the destination in a clockwise direction. Translucent symbols and a classification scheme can be used to make a krisko…
Spatial Autoregressive Model for Population Estimation at the Census Block Level Using Lidar-derived Building Volume Information
The collection of population by census is laborious, time consuming and expensive, and often only available at limited temporal and spatial scales. Remote sensing based population estimation has been employed as a viable alternative for providing population estimates based on indicators that make use of two-dimensional areal information of buildings or one-dimensional length information of roads The recent advancement of LIDAR remote sensing prov…
Modeling Network Autocorrelation in Space–Time Migration Flow Data
Gravity-type spatial interaction models have been popularly utilized in modeling cross-sectional migration data, but their misspecification also has been raised in the literature. This misspecification issue principally concerns an insufficient accounting of underlying effects of spatial structure, including the presence of network autocorrelation among migration flows. Recent studies reveal that spatial interaction models are significantly impro…
Using Bayesian Methods to Control for Spatial Autocorrelation in Environmental Justice Research
Many previous environmental justice (EJ) studies have argued that there is disproportionate collocation of environmental disamenities with racial and ethnic minorities, even holding constant other factors such as income and political action. However, most of the EJ studies do not account for the presence of spatial autocorrelation, especially those that also include nonnormal distributions. Using the location of new Toxics Release Inventory facil…
Modeling Sanction Choices on Fraudulent Benefit Exchanges in Public Service Delivery
Public service delivery programs are not free from players' opportunistic behaviors, such as fraudulent benefit exchanges. The standard methods used to detect such misbehaviors are static, less effective in uncovering interactions between corrupt agents, and easy to evade because of corrupt agents' familiarity with detection procedures. Current fraud detection efforts do not match the dynamics and adaptive processes they are supposed to monitor a…
Space–Time Analysis
Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel …
Measuring Spatial Dependence
Measuring spatial dependence is an essential process for diagnosing if conventional statistical approaches would produce proper results, and/or if spatial statistical methods need to be employed. It involves an operational framework in which spatial dependence is captured, numerical indices and their statistical properties that are used for a significance test, and graphics to visually portray spatial dependence. Because methods measuring spatial…
Optimal Map Classification Incorporating Uncertainty Information
A choropleth map frequently is used to portray the spatial pattern of attributes, and its mapping result heavily relies on map classification. Uncertainty in an attribute has an influence on map classification and, accordingly, can generate an unreliable spatial pattern. Only a few studies, however, have explored the implications of uncertainty in map classification. Recent studies present methods to incorporate uncertainty in map classification …
Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates
The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introd…
Space-time cluster detection with cross-space-time relative risk functions
Space-time kernel density estimation (STKDE) commonly is used for space-time cluster detection. But, this technique might be limited because it does not take into account an underlying population at risk for observed events. A space-time relative risk function (STRRF) can help overcome this limitation by allowing a comparison of each kernel density of observations with that of controls. This paper proposes a cross-STRRF to identify spatio-tempora…
Revisiting environmental inequity in Southern California
This study revisits the concept of environmental inequity in Southern California using the California Environmental Protection Agency’s most recent data and spatial models. Empirical studies in the late 1990s documented the existence of environmental inequity among disadvantaged populations in the area, and we still found evidence of environmental inequity. However, our findings were more nuanced and subtler than previous results. The risk of bei…
Temperature and assault in an urban environment
Deeper Spatial Statistical Insights into Small Geographic Area Data Uncertainty
Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small local sample sizes, which tend to inflate local uncertainty and undermine otherwise sound statistical analyses. This condition is the opposite of that afflicting statistical sig…
Measuring Local Spatial Autocorrelation with Data Reliability Information
Local spatial autocorrelation (SA) measures have been used in exploratory spatial data analysis, particularly in detecting spatial clusters. Existing local SA measures, however, are likely unreliable and biased because they compare only estimates among neighboring spatial units, ignoring errors associated with these estimates. The spatial Bhattacharyya coefficient (SBC) compares probability distributions by considering both estimates and their st…
Modeling Community Health with Areal Data
Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider p…
Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants
A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr), measured during its assaying; errors for these measurements are analyzed in this paper, with an objective of contributing to the geography of…
A Moran eigenvector spatial filtering specification of entropy measures
Modeling Crime Density with Population Dynamics in Space and Time
The current study explores populational and environmental factors associated with violent crime. Specifically, it compares ambient and residential populations with regard to their association with assault density at a fine spatial and temporal unit in a city with socio-economic control variables. The results show that the ambient population are consistently associated with the level of assaults throughout the four time periods in a day, while res…
Assessing Trauma Center Accessibility for Healthcare Equity Using an Anti-Covering Approach
Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient's chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current pl…
Location Planning of Emergency Medical Facilities Using the p-Dispersed-Median Modeling Approach
This research employs a spatial optimization approach customized for addressing equitable emergency medical facility location problems through the p-dispersed-median problem (p-DIME). The p-DIME integrates two conflicting classes of spatial optimization problems, dispersion and median problems, aiming to identify the optimal locations for emergency medical facilities to achieve an equitable spatial distribution of emergency medical services (EMS)…
The Majority Theorem for the Single ( p = 1) Median Problem and Local Spatial Autocorrelation
Except for about a half dozen papers, virtually all (co)authored by Griffith, the existing literature lacks much content about the interface between spatial optimization, a popular form of geographic analysis, and spatial autocorrelation, a fundamental property of georeferenced data. The popular p ‐median location‐allocation problem highlights this situation: the empirical geographic distribution of demand virtually always exhibits positive spati…
Delineations for Police Patrolling on Street Network Segments with p-Median Location Models
Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This pap…
An Efficient Solving Approach for the p ‐Dispersion Problem Based on the Distance‐Based Spatially Informed Property
The p ‐dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named the distance‐based spatially informed property (D‐SIP) method to reduce the problem size of the p ‐dispersion instances, facilitating a more efficient solution while mainta…
A Spatially Informed Solving Approach for the Traveling Salesman Problem
The traveling salesman problem (TSP) is a combinatorial optimization problem that seeks to determine the optimal route that minimizes the travel cost among a given set of nodes. Because solving the TSP inherently requires an exhaustive search, examining all possible routes to achieve the optimal solution, it becomes computationally challenging, particularly with an increase of problem size. Ensuring optimality while making the problem effectively…
An Efficient Approach for Solving Hub Location Problems Using Network Autocorrelation Structures
The properties of spatial information have been shown to aid in identifying optimal solutions for location–allocation problems. Little effort, though, has been made to develop a spatially informed approach to solving hub location problems, as this class of problems entails a more complex model structure and greater challenges in terms of solving capability. To address this issue, this research proposes the spatially informed hub location problem …
Computer Science (16 obras) · Mathematics (16 obras) · Geography (15 obras) · Statistics (12 obras) · Spatial analysis (9 obras) · Autocorrelation (8 obras) · Econometrics (8 obras) · Population (8 obras) · Data mining (7 obras) · Spatial and Panel Data Analysis (7 obras)