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Everyday mobility is critical to understand migrants' accessibility and inclusions to the destination cities. Existing research was insufficient to explain the everyday movement of relatively privileged skilled migrants from the Global North to the Global South, nor did it distinguish the role of one's capability and intentionality in shaping mobility. To fill in the research gap, we employ the capital-mobility framework to investigate how skilled U.S. migrants' capital accumulation and intentionality jointly affect their everyday mobility in Chinese cities, and the implications for their social inclusion in the local society. We use sketch mapping with semistructured interviews to conduct this study in the Pearl River Delta region, a pioneer of economic development in China. We find that although migrants' capital accumulation facilitated their everyday movement, COVID-19 disrupted such mobility. Whether migrants had intentions to move varied, shaped by geopolitical relations, urban environment, and migrants' own capital. The voluntary or involuntary nature of their everyday mobility also suggests whether their social inclusion or exclusion in the local society is by choice or constraint. Our study integrates geographic information systems visualization with qualitative data to highlight the spatial mobility and immobility at the individual level and generates policy implications for retaining global talent at the city level. [ABSTRACT FROM AUTHOR]

The worldwide growing demand for energy has imposed much pressure on energy supply and the environment. Solar energy, as one of the clean and renewable resources, provides a great potential for helping to meet the growing energy demand and reduce the environmental impacts. How to make the best use of a solar photovoltaic (PV) system has received much attention in recent years. Integrating geographic information systems (GIS), this paper proposes a new spatial optimization problem, the maximal PV panel coverage problem (MPPCP), for solar PV panel layout design. Suitable installation areas are first delineated in GIS. Then the MPPCP is used to identify the best spatial configurations of multiple PV panels. Different orientations and alignment scenarios are incorporated in the model to account for installation constraints while achieving the goal of maximal energy production. The new problem is applied to locate solar PV arrays on a rooftop with limited suitable installation areas. Results show that flexible panel alignments increase the maximal energy production by up to 6%. Model performance and computational efficiency are discussed. The research provides important insights into the spatial layout design of solar PV panels at various scales. • GIS finds the suitable areas for solar PV panel installation. • Layout design maximizes the energy production potential of a solar PV system. • The new method has been applied to identify the optimal panel layout on a rooftop. • Flexible panel alignments increase the maximal energy production by up to 6%. • Model 1 is more computational tractable requiring less problem-solving time. [ABSTRACT FROM AUTHOR]

Abstract: The p‐median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP‐hard problem, the PMP remains challenging to solve optimally, especially for large‐sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high‐quality solutions. In this article, we present a spatial‐knowledge‐enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location‐allocation problems. [ABSTRACT FROM AUTHOR]

Location models have been widely used to support locational decisions for various service provision. One common objective of location models has been to ensure maximal accessibility of sited facilities to demand populations. Accessibility evaluation in location models often assumes that trips originate from fixed locations (usually home) and are single purpose. These assumptions contradict the empirical evidence that suggests trips also commonly originate from non-home locations and may involve multiple stops. In this study, a new multi-objective location model is developed that extents the classic p -median problem (PMP) to account for a more realistic assessment of accessibility. Based on the individual accessibility assessment, notions of trip chaining and activity space are incorporated into the model development. In addition to fixed home locations, stops along chained trips are allowed for potential service site visits, and activity space is introduced as an additional dimension to evaluate accessibility of alternative opportunities. The effectiveness of the new model is demonstrated using an application in Tucson, AZ. [ABSTRACT FROM AUTHOR]

Activity space represents an important concept for understanding human activity-travel. The existing activity space delineation approaches are limited in fully characterizing real-world travel behaviors. To address the issue, this research proposes a new time geography based approach to more accurately portray activity spaces of urban travelers. The proposed approach takes into account the full complexity of real-world travel and underlying urban structures. Results of an empirical study are presented based on the 2008 Add-on National Household Travel Survey conducted in Tucson, Arizona. Activity spaces of 1164 sample travelers are delineated and analyzed. Results show the effectiveness of the new approach in more realistically depicting urban activity-travel. [ABSTRACT FROM AUTHOR]

• Assess the capability of rainwater harvesting (RWH) to meet outdoor irrigation. • A GIS-based methodology implemented at the city scale. • Irrigation demand is largely met by RWH in 32% of the Tucson township sections. • RWH helps meet outdoor irrigation more effectively in typical residential areas. • The proposed RWH systems can lead to a total savings of 13.8 million dollars. The increased demand for water along with limited water supply and climate change has brought about water access challenges globally. Novel and sustainable water management systems are needed for addressing the imbalance between the increasing water demand and limited water resources. Rainwater as a renewable resource presents great potential to help achieve this goal. This study focuses on an important sustainable water management strategy, rainwater harvesting (RWH), and proposes a Geographic Information System (GIS) based method to assess the capability of RWH to meet outdoor irrigation demand at the city scale. High-resolution LiDAR data and orthophotos are used to delineate RWH catchment areas and identify outdoor vegetated sites. Both active and passive RWH are considered in the rainwater supply estimation. Factors including vegetation height, species, density and microclimate variables are incorporated in the outdoor irrigation demand estimation. The approach is applied to study the capacity of RWH in Tucson, Arizona, a semi-arid water stressed city in the Southwestern United States. Results show that the outdoor irrigation in 32% of the Tucson township sections can be met by RWH for more than 8 months of a wet year. The proposed RWH system deployment can lead to an overall savings of 13.8 million U.S. dollars and provides important insights into urban water management and sustainable water planning. [ABSTRACT FROM AUTHOR]

Achieving the greatest coverage using limited resources has long been a concern for regional planners. Since the 1970s, a variety of models have been studied and relied upon. Finding ways to best represent geographical space remains a challenge to many researchers. Solutions suggested by models vary greatly with different space representation schemes. For example, in the past, points have been widely adopted to represent spatial demand for coverage. However, this simple abstraction of geographical space could bring about inaccuracies and uncertainties, and often compromises its solution quality. Considering that demand can be area based, which is beyond points, objects of different shapes have been proposed as an alternative for representation. With advances in geographic information systems ( GISs), the new representation scheme using objects has recently received much attention. Compared with the straightforward point-based abstraction, spatial object representation poses considerable challenges to both model formulation and computation capability. This article revisits model development for the problem of regional coverage maximization and proposes a new formulation where coverage of spatial demand is implicitly modeled. Model testing is conducted through an application to warning siren siting in Dublin, Ohio, which has been studied by other researchers. Results demonstrate the effectiveness of the new model when compared with the existing models. Uno de los objetivos comunes en la planificación regional es lograr la máxima cobertura de un servicio con recursos limitados. Desde la década de los 70s se vienen utilizado una serie de modelos conocidos. Sin embargo, la representación adecuada del espacio geográfico de dichos modelos sigue siendo un problemática para muchos investigadores. Las soluciones derivadas de los modelos varían considerablemente de acuerdo a los diferentes esquemas de representación utilizados. El uso de puntos por ejemplo, ha sido ampliamente utilizado en el pasado como esquema de representación espacial de la demanda a ser cubierta por el servicio. Sin embargo, esta simple abstracción del espacio geográfico puede provocar imprecisiones e incertidumbres que a menudo afectan la calidad de la solución matemática del modelo. En contraste, si la demanda se define como un área que abarca una extensión que va más allá de los puntos es posible proponer-como lo han hecho varios recientemente- el uso de objetos espaciales (de formas variadas) como una alternativa sensata de representación. Con los avances en los sistemas de información geográfica (SIG), este nuevo esquema de representación -que usa objetos especiales- han recibido considerable atención. En contraste con esquemas sencillos como el basado en puntos, la representación de objetos espaciales presenta varios desafíos en cuanto a la formulación y computación del modelo. El presente artículo reseña el desarrollo del modelamiento del problema de máxima cobertura regional ( problem of regional coverage maximization) y propone una nueva formulación en la cual la cobertura de la demanda en el espacio es modelada de manera implícita. La evaluación del modelo se realiza a través de una aplicación que usa datos de la ubicación sirenas de alarma en Dublín, Ohio. Los resultados demuestran la eficacia del nuevo modelo en comparación con los modelos existentes. 利用有限资源实现最大覆盖一直广受区域规划者关注。自20世纪70年代以来，发展了大量针对这一问题的模型研究。寻找最佳的地理空间表达方式仍是研究者面临的一大挑战。不同模型对于不同的空间表达模式变化很大。比如过去空间覆盖需求多采用点集表达。然而，这种简单的地理空间抽象方法可能会引起不精确性和不确定性，通常也会导致解决方案质量下降。考虑到需求可能是基于区域而非基于点，不同形状的对象已经被当作空间表达可替代的方式。随着GIS技术的进步，基于对象空间表达的新方案近来备受关注。较之于直接基于点的空间抽象，空间对象表达在模型的形式化表达和计算性能上均面临相当大的挑战。本文回顾了区域最大覆盖问题模型研究的发展历程，提出了一个新的蕴含空间覆盖建模需求的模型。以曾被诸多学者研究过的俄亥俄州都柏林的报警器选址问题为例对模型进行检验，结果显示新模型较现有模型更为有效。 [ABSTRACT FROM AUTHOR]