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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]

This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as illustrative examples. Many of the individuals contributing to this area of the discipline are identified, demonstrating the breadth of academic institutions spanning the globe where spatial optimization is represented in the research and curriculum of geographers. The article provides a characterization of what a spatial optimization problem is, but also properties, relationships, and challenges behind this. The ultimate purpose of this article is to highlight the spatial optimization subspecialty within geography and in doing so, highlight the need for continued spatial model development and application in the discipline. Further, there is also a need for research focused on techniques to solve spatial optimization problems, particularly in the context of geographic information systems. [ABSTRACT FROM PUBLISHER]

Abstract: It is not uncommon to see maps and more advanced statistical or spatial analysis reported in the media. Geographical information systems (GIS) have no doubt facilitated access to and analysis of spatial information. This article is the outgrowth of contact by the Boston Globe and subsequent published articles examining structure fires and the location of fire stations in Massachusetts. The questions asked by the Boston Globe are important geographical ones regarding location specific urban/rural service provision issues, and to answer these questions requires the use of spatial analysis. This article focuses on the use of methods for answering the questions raised in the context of applying academic research, an issue that continues to be a challenge. [Copyright &y& Elsevier]

• A novel maximum cover location problem is developed for rooftop PV panel layout. • High resolution images of three distinct rooftop structures are collected with a UAV. • Rooftop structure, panel size, and irradiance determine optimal PV panel layout. • Results highlight the importance of considering panel layout for energy estimates. • The framework provides a spatially explicit optimized panel configuration for each rooftop. Recent years have seen a substantial increase in energy produced by renewable sources. The International Energy Agency (IEA) expects a large portion of future growth in renewable energy to come from solar, especially rooftop photovoltaic (PV) systems. Studies focused on estimating rooftop solar energy potential generally use the total area available for PV installation as determined by solar irradiance availability. This process can lead to substantial over- or under-estimation of energy estimates. Only a few studies have incorporated the spatial layout of PV panels in the solar energy generation estimates, and none have simultaneously considered PV panel size, orientation, and rooftop structure. We address this limitation with a new spatially explicit optimization framework to enhance the accuracy of rooftop solar energy assessments. We consider both the roof's structural configuration and the shape and size of the panels in a novel maximum cover spatial optimization model. After applying the framework to three different types of rooftops (flat roof, pitched roof, and complex roof), we find that conventional methods can lead to a nearly 60% overestimation of energy potential compared to the optimized panel layout. Our work illustrates the importance of considering panel size and rooftop characteristics and offers a mechanism for designing more efficient rooftop PV systems. [ABSTRACT FROM AUTHOR]