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Background: Food access is a global issue, and for this reason, a wealth of studies are dedicated to understanding the location of food deserts and the benefits of urban gardens. However, few studies have linked these two strands of research together to analyze whether urban gardening activity may be a step forward in addressing issues of access for food desert residents., Methods: The Phoenix, Arizona metropolitan area is used as a case to demonstrate the utility of spatial optimization models for siting urban gardens near food deserts and on vacant land. The locations of urban gardens are derived from a list obtained from the Maricopa County Cooperative Extension office at the University of Arizona which were geo located and aggregated to Census tracts. Census tracts were then assigned to one of three categories: tracts that contain a garden, tracts that are immediately adjacent to a tract with a garden, and all other non-garden/non-adjacent census tracts. Analysis of variance is first used to ascertain whether there are statistical differences in the demographic, socio-economic, and land use profiles of these three categories of tracts. A maximal covering spatial optimization model is then used to identify potential locations for future gardening activities. A constraint of these models is that gardens be located on vacant land, which is a growing problem in rapidly urbanizing environments worldwide., Results: The spatial analysis of garden locations reveals that they are centrally located in tracts with good food access. Thus, the current distribution of gardens does not provide an alternative food source to occupants of food deserts. The maximal covering spatial optimization model reveals that gardens could be sited in alternative locations to better serve food desert residents. In fact, 53 gardens may be located to cover 96.4% of all food deserts. This is an improvement over the current distribution of gardens where 68 active garden sites provide coverage to a scant 8.4% of food desert residents., Conclusion: People in rapidly urbanizing environments around the globe suffer from poor food access. Rapid rates of urbanization also present an unused vacant land problem in cities around the globe. This paper highlights how spatial optimization models can be used to improve healthy food access for food desert residents, which is a critical first step in ameliorating the health problems associated with lack of healthy food access including heart disease and obesity.

Distance is one of the most important concepts in geography and spatial analysis. Since distance calculation is straightforward for points, measuring distances for non-point objects often involves abstracting them into their representative points. For example, a polygon is often abstracted into its centroid, and the distance from/to the polygon is then measured using the centroid. Despite the wide use of representative points to measure distances of non-point objects, a recent study has shown that such a practice might be problematic and lead to biased coefficient estimates in regression analysis. The study proposed a new polygon-to-point distance metric, along with two computation algorithms. However, the efficiency of these distance calculation algorithms is low. This research provides three new methods, including the random point-based method, polygon partitioning method, and axis-aligned minimum areal bounding box-based (MABB-based) method, to compute the new distance metric. Tests are provided to compare the accuracy and computational efficiency of the new algorithms. The test results show that each of the three new methods has its advantages: the random point-based method is easy to implement, the polygon partitioning method is most accurate, and the MABB-based method is computationally efficient. [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]

Geoforensics leverages various spatial analytical, biological, ecological, and geological techniques to improve criminal investigations, including efforts to thwart terrorism, evaluate humanitarian crises or identify the origin of fraudulent goods. In addition to investigating what took place, geoforensic efforts often focus on determining where and when the event occurred. Forensic palynology is a promising subfield of geoforensics that uses pollen to link persons or objects to particular places and times. This promise is driven by pollen's strong utility as a biomarker. It is ubiquitous, durable and demonstrates a relatively predictable distribution in time and space. As a result, pollen often provides important clues for determining the provenance of hard-to-trace items, such as computers, fraudulent goods, digging tools, clothing, and undetonated explosives during geoforensic investigations. One major limitation that has reduced the implementation of forensic palynology is the lack of computational tools that relate pollen species to geographic locations. The purpose of this paper is to introduce a robust geocomputational framework that uses pollen sample composition to identify the relative likelihood of potential origin locations for improving geoforensic efforts. Using USDA CropScape data for the state of Texas, our results suggest that this framework allowed for multiple potential origin sites to be identified simultaneously, with solution properties that were better than a random process and offer a possible alternative to the single site joint probability approaches commonly used in the field of forensic palynology. • Pollen is small, durable and ubiquitous – making it an excellent biomarker for geoforensics • Pollen can be linked to specific places at specific times • We present geocomputational framework that uses pollen to identify potential origin locations for objects of interest • GOFIND offers an alternative to the single site, joint probability approaches commonly used in forensic palynology. [ABSTRACT FROM AUTHOR]