Spatial Optimization Research Articles

Integrating socioeconomic and ecological data into restoration practice.

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces

Objective. Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch. Approach. Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model. Main results. Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s. Significance. This study provides a new approach that could significantly reduce the brain switch’s FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.

Physical Layer Security Based on 2-D Spatial Position Optimization of Relays in Reflected Indoor VLC System

Physical Layer Security Based on 2-D Spatial Position Optimization of Relays in Reflected Indoor VLC System

Form-Based Code Revisited: Leveraging Geographic Information Systems (GIS) and Spatial Optimization to Chart Commuting Efficiency Landscapes under Alternative City Planning Frameworks

Form-Based Code Revisited: Leveraging Geographic Information Systems (GIS) and Spatial Optimization to Chart Commuting Efficiency Landscapes under Alternative City Planning Frameworks

Facility Layout and Spatial Configuration Efficiency Assessment

AbstractWith rapid regional development and urbanization, many public and private facilities and infrastructures (e.g., sirens, cellphone base stations, bike sharing stations, wind turbines, etc.) require regular renovation or supplementation. Evaluating existing facility efficiency and expanding to new facility locations are of broad interest among stakeholders, including businesses, urban planners, government agencies, and the public more generally. Such evaluation can be used to improve overall social accessibility, equity and efficiency by reconfiguring or adding new facilities in the best way possible. A regularly distributed lattice is often viewed as an optimal configuration given important observed properties and characteristics. In this paper, we formulate a spatial optimization model to evaluate spatial coverage efficiency. Specifically, given two sets of points, the model seeks the optimal location and orientation of an idealized lattice to align with an existing facility configuration. The distance between existing facilities and the ideally configured lattice under the optimal alignment represents efficiency. An iterative heuristic based on gradient descent and spatial indexing is developed to solve this problem. Extensive computational experience demonstrates the importance of this problem and the effectiveness of the derived solution approach, as well as highlights assistance provided to decision makers in identifying inefficiencies as well as improving existing infrastructure service systems.

Analysis of Driving Factors for Vegetation Ecological Quality Based on Bayesian Network

Vegetation is a crucial component of ecosystems, and understanding the drivers and spatial optimization patterns of its ecological quality is vital for environmental management in the middle reaches of the Yangtze River Urban Agglomeration. Traditional evaluations employing single indices may not fully capture the complexity of vegetation elements and require evaluation through various indicators. Therefore, this study introduced the Multi Criteria Vegetation Ecological Quality Index (VEQI), coupled with vegetation cover and vegetation ecological function indicators, to explore the driving factors of vegetation quality in the middle reaches of the Yangtze River and identify key areas where vegetation quality declines or improves. By constructing a Bayesian network for VEQI, we identified the driving variables that influence the index. Additionally, we delineated spatial optimization zones for VEQI. The results indicate that the VEQI exhibits a trend of transitioning from low values in urban centers to high values in suburban and rural areas. Over 20 years, the average VEQI of the study region ranged from 10.85% to 94.94%. Slope, DEM, and vegetation type were identified as significant drivers of VEQI, while precipitation, temperature, and nighttime light were considered secondary factors. Notably, areas in Hunan, Jiangxi, and Hubei provinces, especially the western part of Hunan, were pinpointed as spatial optimization regions. This research not only enhances the understanding of vegetation’s ecological quality in the urban agglomeration of the middle reaches of the Yangtze River but also provides scientific insights for the protection and management of vegetation.

Identification of spatial protection and restoration priorities for ecological security pattern in a rapidly urbanized region: A case study in the Chengdu–Chongqing economic Circle, China

The continuous expansion of modern cities not only leads to ecological degradation but also seriously threatens regional ecological security and sustainable development. The construction of ecological security patterns (ESPs) has emerged as a significant approach to alleviate or even solve the conflict between regional development and ecological protection. The Chengdu–Chongqing Economic Circle (CCEC) represents the core area of regional economic development strategy in western China, characterized by rapid economic growth from 2000 to 2020. This study integrates assessments of ecosystem services importance, eco-environmental sensitivity and landscape connectivity; uses circuit theory and hydrological analysis to establish a research framework for the spatiotemporal evolution of regional ESP; and develops an optimized ESP combined with the Major Function Oriented Zone. The results indicate that urban expansion significantly impacted the ESP of the CCEC between 2000 and 2020. The fragmentation and merging of ecological sources occurred simultaneously, the number of patches reduced by 28.13% from 64 to 46. The early ecological security network was compromised, leading to the disappearance or elongation of some ecological corridors. The number of ecological corridors decreased by 36.03% from 136 to 87; the total length was reduced by 29.92% from 7500.57 km to 5256.28 km. Urgent optimization of the ESP is needed, reducing the number of key ecological protection areas by 50% from 106 to 53 while increasing priority restoration areas by 13.51% from 37 to 42. The study also reveals the insufficiency of the current Major Function Oriented Zone in protecting linear corridors, necessitating focused attention on the protection and restoration of ecological sources and surrounding corridors in important development zones. Additionally, a spatial optimization strategy of "one shelter, two cores, and three regions" is proposed to enhance regional ecosystem stability and connectivity. The aim was to strike a balance between ecological protection and food security by recommending an ecological corridor width range of 30∼100 m. These research findings offer scientific guidance for ecological space protection and restoration in the CCEC, contributing to the enhancement of both scientific and rational ecological planning in rapidly urbanizing areas.

Exponential slime mould algorithm based spatial arrays optimization of hybrid wind-wave-PV systems for power enhancement

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases.

Theoretical optimization of bed packing arrangement in cascade Dual-Catalyst system with side reactions

The performance of cascade dual-catalyst systems is highly related to the catalysts spatial arrangement within the bed layers. However, optimization tools for the cascade dual-catalyst layer arrangements are still in need of development. In this study, an optimization framework based on genetic algorithms (GA) and artificial neural networks (ANN) is developed for optimization while explore effects of side reaction on optimized arrangement. Further, a new descriptor as known as delta switch (DS) is introduced. Isolated analysis of the effects of the two types of side reactions shows that the solely side reaction on S0 results in a repeated S0-S1-S0 structure, while the introduce of side reactions on S1 disrupts this structural regularity and reduces the optimal length of the bed layer. Based on DS patterns, a simplified cascade dual-catalysts spatial optimization framework (≤3 layers) is employed, achieving ≥ 95 % performance of original GA.

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization.

Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues , which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake. We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm. Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption. One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Spatio-temporal Differentiation of County Carbon Budget and Territorial Space Optimization Zoning Strategy in Gansu Province from the Perspective of Carbon Neutrality

Under the background of comprehensively practicing the overall system concept of the "living community" in the new era, incorporating the carbon neutral development goal into the territorial spatial planning and construction and establishing the territorial spatial pattern and optimization strategy in line with the actual development of Gansu Province are of great significance for promoting the comprehensive green low-carbon transformation and high-quality development of regional economy and society. Taking counties in Gansu Province as an example, based on the perspective of carbon neutrality research, the land use carbon budget of 87 counties in Gansu Province in 2010, 2015, and 2021 was calculated and analyzed. GIS spatial analysis and social network analysis were used to further explore their spatial differentiation characteristics and the overall characteristics of the carbon emission spatial correlation network. At last, combined with the main function zoning, the low-carbon oriented land space optimization zoning was carried out, and differentiated low-carbon development strategies were proposed. The results were as follows： ① Carbon emissions in Gansu Province showed an upward trend, but the increase rate decreased, showing a spatial distribution of "high in the central and eastern part of the country, low in the southwest." Construction land was the main carbon source. The carbon uptake showed a spatial distribution of "high in the south and low in the north, high in the west and low in the east." Woodlands were the main carbon sinks. The net carbon emissions showed an increasing trend, and approximately 58.62% of the counties in the province were in a carbon imbalance situation. ② In 2021, the spatial network of county carbon emissions was closely related, showing a "core-edge" pattern. The Chenguan District and Qilihe District were in the core position of the network and received more correlation relationships in the network. The network contacts in Longzhong area were frequent, followed by the contacts in Longdongnan area. ③ Based on carbon emissions, carbon sequestration, and ecological carrying capacity coefficients and using the results of spatial correlation of social networks as role positions, the province was divided into four carbon-neutral sub-districts. At the same time, superimposed analysis of the main function zoning, the county area of the province was reconstructed into seven territorial space zones, and the differentiated regional low-carbon optimization development strategy was proposed for each zone.

Dynamic Evolution of Multi-Scale Ecosystem Services and Their Driving Factors: Rural Planning Analysis and Optimisation

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation.

Symmetry-Optimized Dynamical Analysis of Optical Soliton Patterns in the Flexibly Supported Euler–Bernoulli Beam Equation: A Semi-Analytical Solution Approach

This study employs spatial optimization principles to investigate the nonlinear vibration of a flexibly supported Euler–Bernoulli beam, a (1 + 1)-dimensional system subjected to axial loads. The modified Khater method, a crucial tool in mechanical engineering, is utilized to analyze analytical solutions, which include a symmetric spatial representation of the waveform as an integral part of each solution. Notably, periodic soliton solutions for the nonlinear model closely align with numerical and approximate analytical solutions, demonstrating the accuracy of our modeling approach. Density diagrams, contour diagrams, and Poincaré maps depicting the obtained analytical solutions are presented to elucidate their accuracy and provide visual confirmation of the optimized engineering model’s physical significance. The planar dynamical system is derived through the Galilean transformation by employing mathematical models and appropriate parameter values, thereby further refining problem understanding. Sensitivity analysis is conducted, and phase portraits with equilibrium points are illustrated by analyzing a special case of the investigated dynamical system, emphasizing its symmetrical properties. Lastly, we perform a global analysis to identify periodic, quasi-periodic, and chaotic behaviors, with an extra weak forcing term confirmed by Poincaré maps and a two-dimensional symmetric basin of the largest Lyapunov exponent.

Integrating ecosystem services, stakeholders’ perspective, and land-use scenarios to safeguard sustainability of the Mulberry-Dyke and Fish-Pond System

ContextOptimizing landscape patterns by considering ecosystem services and human well-being is crucial for landscape sustainability research. Such optimization process shall also consider multiple stakeholders’ perspectives, interests and benefits, which remain less well investigated.ObjectivesThe study aims to integrate ecosystem services and land-use scenario analysis to design sustainable landscapes. The overall goal of spatial optimization is to explore pathways towards sustainable development.MethodsThis study uses the Mulberry-Dyke and Fish-Pond System as a representative agricultural landscape. It analyzes the spatiotemporal pattern of the landscape from 1975 to 2019. The market value method, InVEST model, and MaxEnt model are used to quantify the ecosystem services of the landscape. The analytic hierarchy process is used to set the weight of ecosystem services from the perspective of three stakeholder groups. We compare the outcomes of different scenarios and assessed through emergy yield ratio, environmental loading ratio, and environmental sustainability index.ResultsOur results indicated that: (1) The scale of the Mulberry-Dyke and Fish-Pond system decreased significantly from 1975 to 2019. (2) Among the six major ecosystem services, the average value of water purification service was the highest, while average value of mulberry supply service was the lowest. (3) Spatial optimization of the three design objectives, ‘sustainably intensify’, ‘increase landscape multifunctionality’, and ‘restore ecological integrity’, were primarily distributed in the study area’s eastern, central, and western regions, respectively. (4) All three stakeholder-oriented scenarios showed improvements in ecological quality or agricultural output.ConclusionsOur study incorporates viewpoints of critical, diverse stakeholders in the assessment of ecosystem services and scenario analysis to establish sustainable and culturally important agricultural landscape. The framework and methods can help minimize environmental stress on the system, balance agricultural productivity and profitability, and enhance the sustainability of agricultural landscapes.

Optimization of spatial layouts for underground facilities to achieve carbon neutrality in cities: A multi-agent system model

Subways, underground logistics systems and underground parking, as the primary facilities types of underground, contribute significantly to the achievement of carbon–neutral cities by moving surface transportation to underground, thereby releasing surface space for the creation of more urban blue-green space for carbon sink. Therefore, in-depth studies on carbon neutrality strategies as well as reliable layout optimization solutions of these three types of underground facilities are required. This study proposes a spatial layout optimization strategy for carbon neutrality using underground hydrogen storage and geothermal energy for these three types of underground facilities employing a multi-agent system model. First, three spatial layout relationships, competition, coordination, and followership, between five underground facilities that contribute to emission reduction were investigated. Second, the implementation steps for optimizing the spatial layout of underground facilities were determined by defining the behavioral guidelines for spatial environment, underground facility, and synergistic agent. Finally, using the Tianfu New District in Chengdu City, China, as a case study, layouts of underground facilities under three different underground space development scenarios were simulated to verify the model. The findings of this study address the gap in the research on underground spatial facilities and their layout optimization in response to emission reduction. This study provided a significant reference for the study of underground space and underground resources at the planning level to aid in achieving carbon–neutral cities.

Micro-Scale Analysis and Optimization of Rural Settlement Spatial Patterns: A Case Study of Huanglong Town, Dayu County

Optimizing the spatial patterns of rural settlements is crucial for rural development and revitalization. Enhancing the internal spatial configuration of these settlements necessitates a comprehensive understanding of their micro-scale spatial characteristics. This study develops evaluation indicators and methodologies to quantify rural settlement spatial patterns by analyzing their multidimensional aspects. The research utilizes Huanglong Town in Dayu County, Jiangxi Province, as a case study for exploring micro-scale spatial patterns and proposing corresponding optimization models. The research employs remote sensing image processing and GIS spatial analysis to collect data on the study area. The results indicate that rural settlements in Huanglong Town generally form clustered patterns with moderate spatial structure intensity and order. Notably, spatial heterogeneity is observed across the northern mountainous area, the central plain and low hilly region, and the southern hilly area. Based on these findings, the study categorizes rural settlements in Huanglong Town into four optimization models: stable improvement, internal potential exploitation, controlled expansion, and relocation and withdrawal. Each model is associated with differentiated optimization strategies. By integrating analyses of spatial form, structure, and order, this study reveals the intrinsic spatial characteristics of rural settlements, offering a systematic approach to guide their spatial optimization.

A Novel Learning-Based Trajectory Generation Strategy for a Quadrotor.

In this article, a learning-based trajectory generation framework is proposed for quadrotors, which guarantees real-time, efficient, and practice-reliable navigation by online making human-like decisions via reinforcement learning (RL) and imitation learning (IL). Specifically, inspired by human driving behavior and the perception range of sensors, a real-time local planner is designed by combining learning and optimization techniques, where the smooth and flexible trajectories are online planned efficiently in the observable area. In particular, the key problems in the framework, temporal optimality (time allocation), and spatial optimality (trajectory distribution) are solved by designing an RL policy, which provides human-like commands in real-time (e.g., slower or faster) to achieve better navigation, instead of generating traditional low-level motions. In this manner, real-time trajectories are calculated using convex optimization according to the efficient and accurate decisions of the RL policy. In addition, to improve generalization performance and to accelerate the training, an expert policy and IL are employed in the framework. Compared with existing works, the kernel contribution is to design a real-time practice-oriented intelligent trajectory generation framework for quadrotors, where human-like decision-making and model-based optimization are integrated to plan high-quality trajectories. The results of comparative experiments in known and unknown environments illustrate the superior performance of the proposed trajectory generation strategy in terms of efficiency, smoothness, and flexibility.

Analysis of temporal and spatial dynamics and driving factors in the aquaculture industry of Fuding City, China

BackgroundThe sustainable development of aquaculture is a crucial determinant of food security, the well-being of aquaculture practitioners, and economic growth within coastal regions. Considering the existing gap in research regarding spatial and temporal development of aquaculture, this study investigates the progression of aquaculture practices over time and across various locations in Fuding City, China. MethodsWe retrospectively collected temporal and spatial data on aquaculture, as well as demographic, social, and economic data for Fuding City from 2010 to 2020. By employing 3D kernel density analysis, we illustrated the temporal and spatial changes in aquaculture. Furthermore, we utilized Ordinary Least Squares regression to investigate the driving factors behind the spatial changes in the aquaculture industry. ResultsOver the past decades, we observed that in Fuding City, both the number of fishing rafts and aquaculture households initially decreased and then increased. The spatial distribution of aquaculture experienced a shift from the west (inner bay area) to the east (coastal area). Additionally, the type of fishing rafts also varied by region, with traditional rafts dominating the western inner bay and plastic rafts prevalent in the eastern offshore areas. Analysis of driving factors revealed that at least six factors have a significant positive correlation with the eastward shift of the aquaculture industry's center, including the proportion of migrant population, proportion of aquaculture to total fishery output, average temperature, investment in aquaculture technology, total fish sales, and GDP of Fuding City. ConclusionThis study examines the spatial and temporal dynamics of aquaculture in Fuding City from 2010 to 2020, proposing an innovative approach to spatial optimization that integrates both horizontal and vertical strategies. These insights aim to guide the development of coastal aquaculture policies and support sustainable regional development, fostering a balanced coexistence between human activities and marine environments.

Investigation on construction characteristics and indoor environmental quality of Fangcang shelter hospitals during the COVID-19 pandemic in Shanghai, China

Since the rapid spread of COVID-19 in late 2019, Fangcang shelter hospitals have become crucial components of urban healthcare facilities. However, there remains a lack of systematic research on the performance assessment of Fangcang shelter hospitals. This study evaluated the construction efficiency, facility allocation, and indoor environmental quality (IEQ) of 115 Fangcang shelter hospitals in Shanghai. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, 51,040 social media reviews were processed to evaluate the prevalence of IEQ complaints. The results show that Fangcang shelter hospitals renovated from exhibition halls have the fastest median construction speed (58.2 bed/h), closely followed by office buildings (41.7 bed/h), while containers were the slowest (9.1 bed/h). Notably, there is a power function relationship between the bed capacity and construction speed (R2 = 0.81), indicating a scale effect. As for the indoor environment, toilet comfort issues comprise 31.2 % of the total complaints, followed by complaints about acoustics (22.8 %), hygiene (12.0 %), and thermal (11.2 %). Container-built Fangcang shelter hospitals face significant challenges to the acoustic (40 %) and thermal (15 %) environment, while renovated facilities encounter more problems with toilet facilities and privacy. We also found that the proportion of indoor air quality (IAQ) issues increases as the average bed area decreases across facilities renovated from factories, exhibition halls, and office buildings. These findings offer valuable insights into Fangcang shelter hospitals, which can be considered in future planning and spatial optimization of emergency shelters.

A location analytics perspective of regional science at a crossroad

Location analytics have been a mainstay in regional science since its inception. Why? Perhaps the biggest reason is the ability to tease out important spatial heterogeneity nuances, but also to communicate geographically oriented analysis, planning, management and policy issues in rigorous ways, often as mathematical models, that can be subjected to further scrutiny and evaluation. Further, addressing broad challenges facing emergency response, food production, risk mitigation, transportation, public health, crime, housing and other issues through such approaches has proven an immense benefit to society. This paper focuses on the evolving subfield within regional science of location analysis and modeling, highlighting contributions to date but also detailing exciting areas of advancement involving spatial optimization and geographic information science. The paper includes a reflection on regional science journal publication trends, but also broader patterns of location analytics activity. It is hoped that this review will lead to a reassessment of regional science priorities, and consideration of the role that our specialty areas have in maintaining core strengths as well as potential for growing the discipline in the coming years.