This paper investigates the determinants of spatial uncertainty associated with respondent reporting of potential HIV risk activity, lifestyle, and healthcare locations in Los Angeles. We examine which geographic and socio-demographic variables are associated with the accuracy of location reporting. Significant variables associated with activity space reporting accuracy related to HIV risk and HIV prevention are identified. The study is based on gay, bisexual, and other sexual minority men (SMM) enrolled in the UCLA mSTUDY, collecting demographic characteristics and locations of recent activities. An exploratory spatial data analysis is carried out. Statistical modeling with logistic regression was used to identify sources of uncertainty and their influence on reporting accuracy. This paper concludes that demographic characteristics, activity types, and geographic location are significant predictors of reported geographic accuracy. The research also found regional variation in accuracy, with locations within regions reported with higher accuracy than those outside them. Future work aims to refine statistical models of location accuracy and establish confidence intervals to mitigate uncertainty, which is needed to improve the effectiveness of interventions aiming to reduce HIV incidence disparities.

The classical transportation problem is an important urban and regional planning and policy-making analytic tool as it has the potential to provide deep insights into the competitive advantage of locations for different activities. While the classical transportation problem and its variants have been extensively employed in commuting efficiency studies, insufficient attention has been paid to its mathematical dual. This paper aims to revisit the spatial interpretation of the duality of urban location modeling from the perspective of location rent theory. In doing so, the study integrates geovisualization and spatial analysis with the dual of a variant of the classical transportation problem to measure, interpret, and chart location rent surfaces for origins and destinations associated with realized rush-hour work trips across three economic sectors in a metropolitan region in Central Florida. Sensitivity analysis is conducted to determine the effects of spatial variability in the levels of workforce and employment in each economic sector for commuting efficiency. The spatial congruence between location rents and future land use under an alternative planning regime is evaluated at both regional and neighborhood scales. Results indicate that allocating future urban growth in residential and employment zones with the highest and lowest location rents, respectively, yields the most favorable jobs-housing balance. The findings also suggest that dual values derived from a spatial optimization problem can function as both descriptive and prescriptive tools, supporting the evaluation of planning initiatives, urban spatial structures, and both tactical and long-term land-use policies and growth management strategies.

This research addresses a critical but underexamined challenge in disaster preparedness: the identification of neighborhoods where evacuation is hardest to accomplish based on physical infrastructure constraints as well as inherent social vulnerability. Existing evacuation approaches emphasize the flow of traffic and the capacity of roads, but typically presume equal capability for people to evacuate, neglecting the aggregate barriers faced by socially vulnerable populations. Closing this gap, the research proposes an equity-informed approach aided by spatial optimization to identify population clusters with evacuation bottlenecks. In collaboration with local community groups and emergency response agencies in Santa Barbara County, this research informs inclusive evacuation strategies that foster resilience and lessen structural disparities in disaster response.

The shape of an area is an important geographical concept that is largely defined by its degree of being compact. As a result, much attention and focus has been on the best approach(es) to measure compactness in order to assess the relative characteristics of an area. Geographers have made important contributions to the specification of measures and metrics associated with compactness. In fact, the radial deviation approach proposed by Boyce and Clark (Geogr Rev 54(4):561–572, 1964) remains particularly popular and broadly applied. This paper investigates this measure with respect to properties and theoretical relationships. A GIScience based generalization is possible that extends the radial sampling and approximation notion to a closed form exact approach. Empirical assessment explores the merits of this generalization with respect to implementation and application in GIS. The findings highlight that existing approaches do not accurately estimate average radial distance from a center to shape boundaries, creating bias and misleading compactness measurement. Further, the findings demonstrate that this can be overcome through the proposed generalization and extension relying on the actual average radial distance.

Live fuel moisture (LFM) is a critical determinant of wildfire behavior, especially in Southern California chaparral, yet spatially continuous and near-real-time estimates remain limited by sparse field measurements, and spatial transferability challenges. In addition, the transition from MODIS to VIIRS requires evaluation of the continuity of long-term satellite-based LFM monitoring. This study developed a framework using 2003-2022 Globe-LFMC 2.0 dataset that first compared MODIS-based multiple linear regression and random forest models, then applied bias correction, and transferred the framework to VIIRS to assess cross-sensor continuity. This framework was further extended for near-real-time application by integrating analog-year phenology estimation and survival-based dry-down timing estimation. For MODIS, random forest achieved higher accuracy with the full dataset, but its performance declined substantially under leave-one-county-out spatial cross-validation and showed reduced ability to capture low and fire-disturbed LFM values. Multiple linear regression showed more stable performance between full-dataset evaluation (R2 = 0.63, RMSE = 12.35%) and spatial cross-validation (R2 = 0.58, RMSE = 12.43%), indicating greater spatial transferability. MODIS models provided higher predictive skill overall than VIIRS, whereas the M-band-only VIIRS configuration produced the highest VIIRS performance and reproduced major seasonal and spatial LFM patterns. Independent validation using 2023-2024 Fire Environment Mapping System (FEMS) dataset showed that LFM thresholds of 85-90% provided the most balanced classification of elevated-risk conditions for both MODIS and VIIRS. This study shows that an interpretable, phenology-informed satellite framework can support spatially transferable and temporally consistent LFM monitoring for chaparral fire risk assessment.

The structure of urban regions evolves with population growth and development, but in doing so inherent risks and dangers emerge. This is especially true in the wildland urban interface where it is not uncommon to find significant clusters of homes with limited egress, where people are forced to exit (or enter) through a restricted number of access roads. Limited points of entry/exit are bottlenecks that may create congestion, but also amplify risk and vulnerability during an hazard event, such as fire, flood, toxic release, severe weather, volcanic eruption, tsunamis, landslide, debris flow, sink hole, unexploded ordnance, improvised explosive devices, or other emergencies when evacuation is necessary. If bottlenecks are obstructed or otherwise inoperable, then neighborhood clearing is hampered. The paper introduces spatial analytics combined with geographic information systems that support assessment and planning efforts associated with hazards that may necessitate evacuation. An assessment of coastal communities in Santa Barbara County, California, is reported to demonstrate current capabilities in identifying neighborhoods that are potentially difficult to vacate and their associated bottleneck street segments that limit egress. The paper outlines future research needs to better support safety and security efforts as well as legal mandates in the assessment and mitigation of evacuation risk and vulnerability.

Natural and man-made disasters are generally characterized in terms of their human-environment interactions. Wildfire, or simply fire, is something that has naturally occurred over millions of years, but it is the interaction, disruption, and impact on humans that often generates much interest and concern. The growth of the built environment and human reliance on fire in everyday life can be a dangerous combination, putting people and property at significant risk. To address this, various mitigations and adaptation strategies have emerged, including the enactment of regulations, building codes and standards, response capacities, and expansion of critical services and infrastructure that seek to mitigate the risks of fire. This article offers an overview of human-environment interactions with fire that includes its historical role along with the evolution of both structure and wildland safety codes and standards, human development practices, and firefighting policies. Highlighted is firefighting that emerged as a by-product of the insurance industry to something that is now a fundamental public service. A primary focus in this article is on the spatial aspects of fire safety standards and guidelines to mitigate fire risk due to the intermixing of the natural and built environments along with human behaviors that exacerbate vulnerability.

Rural fire response in the USA continues to depend heavily on volunteer personnel, whose numbers have been in decline for decades. Yet, the extent and geography of volunteer staffing decline remain difficult to assess. This study examines volunteer personnel capacity in rural California using multi-source quantitative and qualitative evidence, including the National Fire Department Registry and public documents such as municipal service reviews and civil grand jury reports. The results show that rural California remains highly dependent on volunteer staffing and that documented volunteer decline is widespread, though spatially uneven, across much of the state. In many jurisdictions, documents report recruitment and retention difficulties, reduced staffing availability, or operational consequences like lower response rates and station closures. Taken together, these findings suggest that volunteer staffing decline is not limited to isolated local cases and may affect the continuity, reliability, and spatial coverage of rural emergency response. The study also identifies commonly cited policy responses, but their reported implementation remains selective and fragmented in rural California. These results provide an initial statewide evidentiary baseline for management, policy, and future research on rural fire response capacity.

The selection of areas for land use activity has long been of interest in environmental modeling. Such areas have been referred to as patches, blocks, districts and zones, representing activities like nature preserves, harvest operations, hazard mitigation, residential developments, industrial processing plants and storage facilities, among others. Many important spatial attributes of these areas are often necessary or desired, including compactness, contiguity and dispersion. This paper introduces a modeling approach to simultaneously address these goals. A linear integer model is structured to optimize total activity benefit along with compactness while ensuring contiguity and size restrictions among individual areas. Application results highlight environmental mitigation patches designed to reduce regional wildfire risk and vulnerability. Further, the role of perimeter as a surrogate for addressing compactness and consistency with alternative measures and metrics of compactness are considered.

Natural and man-made disasters have a significant impact on movement patterns, yet existing studies often focus on aggregate-level trends, overlooking the heterogeneity of mobility responses within and across communities. A critical gap remains in understanding the nuanced impacts of wildfires on localized, fine-scale human mobility patterns, particularly how factors such as proximity to the fire, socio-demographic characteristics, and infrastructure shape movement responses. In this paper, we address this gap by analyzing human movement patterns during the wildfire seasons from 2018 to 2020 in California Spatial analytics are used to examine movement flows around twelve wildfire events and map the evolution of their spatio-temporal patterns in response to these disruptive events. To quantify and assess the magnitude of changes in movement patterns between fire and non-fire years, we employ a structural similarity metric. In doing so, we identify the points of influence corresponding to the onset of the fire and the subsequent recovery period. Finally, we characterize variations between fire events and identify the wildfires that had the most significant impact on movement patterns. The findings demonstrate the complexity of movement responses to the wildfires, highlighting the intricate interactions between visitation patterns and the built environment.

The importance of good locational decision making cannot be understated. In many cases it is quite literally a question of life and death, whether in the context of safety and security or associated with the viability of business activity. As a result, location modeling has become essential in system understanding, designing or extension in whatever way spatial choice is considered. Location modeling too is central in addressing sustainability, resilience, efficiency and effectiveness across a range of urban and environmental contexts. Over the past three decades geographic information systems, and more generally GIScience, has emerged as a critical complement to location modeling. This paper seeks to articulate and demonstrate how GIScience is now a central component of location modeling, one that fundamentally bridges geographic information systems and optimization in many ways. GIScience primitives are formally structured and specified in order to make linkages explicit in the context of location modeling. This is significant as GIScience helps to further establish locational theory and principles that form the basis of model extension as well as enables better solution approaches to be developed. Because of this, continued integration of location modeling and GIS is anticipated in the coming years and decades.

For gay, bisexual, and other sexual minority men (SMM), geo-social exposures in residential and non-residential places are important to consider for health, as home, social, sexual, substance use, and healthcare-related locations may be different. We use survey data from a sample of 219 Black and Hispanic SMM within Los Angeles County to examine the places that individuals visit for eight specific activities, categorized as either lifestyle or healthcare-related. Spatial clustering techniques are used to identify hotspots, or places where individual's activities are clustered in space, for each activity. We then use descriptive statistics to characterize each hotspot based on the socio-demographic characteristics of individuals who engaged in activities within the hotspot, and then assess whether activity-based hotspots overlap in space. We find unique spatial patterns of hotspots, distinct by activity. Additionally, lifestyle activity space hotspots are spatially patterned by socio-demographic characteristics, primarily along race and ethnic categories, whereas healthcare-related hotspots are not. The overlap, or spatial congruence of hotspots, is higher than we hypothesized, as hotspots of residential locations contained the majority of sex hotspots and substance use hotspots. Our work ultimately identifies four distinct areas of Los Angeles County in which activities are clustered among men in the sample, and health interventions can be tailored to the individuals and their activities in those places. Our findings demonstrate the importance of geographically and demographically targeted interventions, at a fine spatial scale, for health promotion among SMM, as interventions and policy to provide equitable care to reduce racial disparities in health among SMM are sorely needed.

Demographic and socio-economic omissions in social vulnerability assessments are not uncommon. However, conclusions from incomplete population characterization contribute to inequity and marginalization of at-risk segments of society, individuals who require care, resource assistance, and tailored public policy. The unsheltered homeless are considered here as an underrepresented segment of socially vulnerable people in hazard assessment. The unsheltered suffer disproportionately higher levels of direct exposure to natural and human-caused disasters, yet their lack of stable housing and often frequent moves make them both hidden and invisible in traditional census information. An exploratory spatial data analysis framework is developed to combine geographic information systems (GIS), statistics, and geostatistics to assess the implication of this omission. This research treats the integration of annual government counts and continuously updated, community-contributed information as a means of capturing regional spatial patterns of unsheltered homelessness at a fine geographic level. Comparison is then made to approaches based on traditional social vulnerability measures to evaluate consistency and representativeness. The findings suggest a significant gap in existing approaches. This study sheds light on potential avenues for more inclusive and accurate assessments of social vulnerability in the context of hazards.

Modern forest management is often focused on minimizing wildfire risk by regaining fire-resiliency from historical conditions. This process is labor and resource intensive and it is usually infeasible to treat entire forests. Therefore, forest treatment allocation optimization is applied to help identify the best mitigation plan, with a key objective being the minimization of departure from historical landscape conditions. However, the vagueness and imprecision of historical landscape condition models and their corresponding departure indicators due to data limitations often necessitate the use of simulated substitutes. Since spatial optimization, integral for identifying optimal mitigation plans, relies on simulation outputs, addressing uncertainty becomes a crucial concern. We analyze optimization outputs for different historical condition scenarios and propose strategies to identify robust alternatives in two steps. First, we conduct a series of spatial optimizations using a weighted-sum method, systematically varying the importance assigned to different objectives for each scenario of historical conditions. This process helps us understand how changes in the assumptions about past forest conditions and the relative weights assigned to each assumption affect the optimal management plans. Second, we analyze the resulting solutions to identify patterns and overlaps among the optimal spatial configurations across scenarios. By focusing on the identification of common patterns, we develop a strategy to select solutions that are robust to uncertainty in historical condition models. We find that the strategy based on identifying commonalities among optimal solutions yields management plans that are, on average, 8.9% more robust compared to plans derived without explicit consideration of robustness. We conclude that the commonality seeking strategy in solution space alleviates risk in decision making. These insights highlight the importance of developing strategies for applying robust strategies in spatially explicit optimization problems.

The field of location analytics has long recognized coverage and accessibility as critical problem characteristics, as reflected in the work of Weber and von Th & uuml;nen, among others. Attempts to address various associated complications, assumptions, and issues abound, including uncertainty, spatial representation, discrete approximation, and heuristic and exact solution, just to name a few. This article investigates unidentified continuous demand, exploring the impact of discrete approximation when one or more attributes vary across space. A multiple-objective location model is applied to evaluate capabilities in accurately and completely identifying nondominated solutions that simultaneously optimize coverage and access. Spatial analysis is carried out to comparatively assess findings across a range of discretization approaches. The results are significant, offering important insights into spatial scale impacts and application-oriented studies. This work elucidates the influence of spatial relationships and scale in location analytics.

Planning support approaches can play a transformative role in shaping sustainable, resilient, and equitable urban landscapes that promote efficient mobility patterns. This paper develops a prescriptive framework that integrates Geographic Information Systems with a variant of the transportation problem to evaluate planning initiatives and inform land use policies and growth management strategies aimed at enhancing commuting efficiency at both local and regional levels. A multi-step approach is structured including 1) a multi-objective spatial optimization model that simulates the impacts of alterations to urban locational structure on work trip durations, capturing normative commuting patterns across three major workforce groups under varying urban growth scenarios; and 2) a modified gravity model that estimates regional commuting efficiency at the economic-sector level based on optimized inputs. Results indicate that this framework enables a critical evaluation of urban spatial configurations and corresponding commuting efficiency indicators under both conventional and alternative planning systems. The proposed framework also supports tactical and strategic land use and transportation planning, allowing planners and policymakers to analyze potential urban forms across different development scenarios, dissect commuting efficiency outcomes by industry, and identify sectors with critical spatial mismatches between job locations and housing. The ability to guide more balanced urban development and foster more efficient commuting patterns is demonstrated for Central Florida.

Exposure to a range of hazards is a fact of life, but risk and vulnerability varies based on where you live, the places you frequent and your associated socio-economic characteristics. Many hazards trigger community responses that involve evacuation of people and property, a proven action that reduces the loss of life and injury. Effective and efficient evacuation requires many interdependent operations, including community notification, transportation assistance, management of traffic flows and safe locations to harbor people. However, this must be focused on those neighborhoods that are or could be in danger. Methods to both formalize and define evacuation neighborhoods, or critical clusters, are detailed. A new spatial optimization model is introduced for identifying these neighborhoods as well as their associated exit roads that may restrict traffic flow during an emergency. Evacuation vulnerability in coastal Santa Barbara is undertaken, demonstrating the heterogeneous risk that communities face due to inherent bottlenecks in transportation infrastructure. The findings highlight that there are many potentially difficult neighborhoods to evacuate during an emergency, and it is critical that these areas have operational plans in place for ensuring that exit roads remain functional, free of accidents or other disruptions, to ensure the safe movement of people.

Siting facilities strategically is critical to ensure system design efficiency, enhance social equity and reduce operational costs. Coverage models look to optimize facility configuration, often to minimize the number of necessary facilities or maximize demand served within established proximity standards. Existing location models, such as maximal covering, often assume facility standards to be isotropic, resulting in a perceived circular service area. However, many facility proximity contexts, such as travel time on a transportation network, sound propagation, surveillance cameras and non-vertical lights, have irregular or noncircular service areas, potentially complicating existing coverage modeling approaches. Additionally, anisotropic coverage of facilities raises the issue of how to orient them when sited. The goal of this paper is to extend existing approaches to account for anisotropic coverage, simultaneously locating and orienting facilities. A location model is formulated to address anisotropic service coverage of facilities. A finite dominating set is derived, enabling reformulation as an integer programming problem that can be solved via branch and bound. Applications involving emergence response and surveillance camera placement in both 2-D and 3-D spaces demonstrate the effectiveness of this modeling extension. The resultant anisotropic coverage model addresses a critical aspect of system performance, highlighting that the omission of such considerations greatly overestimates what may be achieved in operation.

In safety planning, preparing for worst-case scenarios is critical. For instance, fire stations are strategically located aiming to respond within four minutes in the worst-case. Similarly, hydrant-to-structure access adheres to this principle. Fire codes require that the furthest projection on a building's exterior must be within a specified distance from fire access roads via an unobstructed route. This ensures that all parts of a building are reachable by a fire hose from parked fire apparatus. This requirement involves a novel spatial optimization problem: the Maximum Generalized Euclidean shortest path problem. The Euclidean shortest path problem is an approach for determining an unobstructed shortest path, however, constrained to single-point representations for origin and destination. This research generalizes this problem to identify unobstructed paths between multipart-continuous geometries, such as road segments and building structures. A novel solution approach is also proposed, expanding the scope of access evaluation and advocating safety planning.