Gardening in the desert: a spatial optimization approach to locating gardens in rapidly expanding urban environments.

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.

GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation.

Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phenomena, e.g., why is crime higher in this locale, why do children in one school district outperform those in another, etc.? Answers to these questions require explanations of the model structure, the choice of parameters, and contextualization of the findings with respect to their geographic context. This is particularly true for local forms of regression models which are focused on the role of locational context in determining human behavior. In this paper, we present GeoExplainer, a visual analytics framework designed to support analysts in creating explanative documentation that summarizes and contextualizes their spatial analyses. As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results. As analysts explore the model results, all visualizations and annotations can be captured in an interactive report generation widget. We demonstrate our framework using a case study modeling the determinants of voting in the 2016 US Presidential Election.

Assessing the impact of technological change on similar occupations: Implications for employment alternatives.

BACKGROUND: The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. METHOD: We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. RESULTS: The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. CONCLUSION: Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.

Global shocks, cascading disruptions, and (re-)connections: viewing the COVID-19 pandemic as concurrent natural experiments to understand land system dynamics.

Context: For nearly three years, the COVID-19 pandemic has disrupted human well-being and livelihoods, communities, and economies in myriad ways with consequences for social-ecological systems across the planet. The pandemic represents a global shock in multiple dimensions that has already, and is likely to continue to have, far-reaching effects on land systems and on those depending on them for their livelihoods. Objectives: We focus on the observed effects of the pandemic on landscapes and people composing diverse land systems across the globe. Methods: We highlight the interrelated impacts of the pandemic shock on the economic, health, and mobility dimensions of land systems using six vignettes from different land systems on four continents, analyzed through the lens of socio-ecological resilience and the telecoupling framework. We present preliminary comparative insights gathered through interviews, surveys, key informants, and authors' observations and propose new research avenues for land system scientists. Results: The pandemic's effects have been unevenly distributed, context-specific, and dependent on the multiple connections that link land systems across the globe. Conclusions: We argue that the pandemic presents concurrent "natural experiments" that can advance our understanding of the intricate ways in which global shocks produce direct, indirect, and spillover effects on local and regional landscapes and land systems. These propagating shock effects disrupt existing connections, forge new connections, and re-establish former connections between peoples, landscapes, and land systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-023-01604-2.

Enhancing the Jacquez k nearest neighbor test for space-time interaction.

The Jacquez k nearest neighbor test, originally developed to improve upon shortcomings of existing tests for space-time interaction, has been shown to be a robust and powerful method of detecting interaction. Despite its flexibility and power, however, the test has three main shortcomings: (i) it discards important information regarding the spatial and temporal scales at which the detected interaction takes place; (ii) the results of the test have not been visualized; and (iii) recent research demonstrates the test to be susceptible to population shift bias. This study presents enhancements to the Jacquez k nearest neighbors test with the goal of addressing each of these three shortcomings and of improving the utility of the test. Data on Burkitt's lymphoma cases in Uganda between 1961 and 1975 are used to illustrate the modifications and enhanced visual output of the test. Output from the enhanced test is compared with that provided by alternative tests of space-time interaction. Results show the enhancements presented in this study transform the Jacquez test into a complete, descriptive, and informative metric that can be used as a stand-alone measure of global space-time interaction.

Comparative modeling approaches for understanding urban violence.

The purpose of this paper is to provide a comparative analysis of three different modeling approaches for exploring structural theories of violence. Specifically, ordinary least squares regression, geographically weighted regression and data envelopment analysis will be utilized to evaluate violent crime. This type of analysis expands upon traditional theory testing by deepening our understanding of differences in crime generation and its underlying demographic and socio-economic stimuli via different methodological lenses. A case study for the city of Cincinnati, Ohio is presented and the results suggest that a combination of approaches is likely the best strategy for evaluating violence in urban areas.

The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis.

The COVID-19 pandemic caused a variety of social, economic, and environmental changes. This paper examines the employment-related impacts of the pandemic on workers in the transportation industry compared to other industries, and within different transportation sectors. We estimated random effects logistic regression models to test the following three hypotheses using the monthly Current Population Survey micro-data. One, the transportation industry experienced a greater incidence of unemployment than other industries. Two, there is heterogeneity in employment impacts within the transportation sector. Three, specific sectors within the transportation industry experienced more employment impacts than other essential industries, as designated by the Centers for Disease Control and Prevention (CDC) Phase 1a vaccination guidelines. Model results highlight that workers in the transportation sector were 20.6% more likely to be unemployed because of the pandemic than workers in non-transportation industries. Model results also indicate large intra-sector heterogeneities in employment impacts within the transportation sector. Taxi and limousine drivers were 28 times more likely to be unemployed compared to essential workers. Scenic and sightseeing transportation workers were 23.8 times more likely to be unemployed compared to essential workers. On the other end of the spectrum, however, postal workers and pipeline workers were 84% and 67% less likely to be unemployed compared to essential workers, respectively. From a policy perspective, these results suggest that attention to several aspects of transportation work is needed in the coming years to prepare for future interruptions to the transportation industry.

Conflict and its relationship to climate variability in Sub-Saharan Africa.

Deviations in rainfall duration and timing are expected to have wide-ranging impacts for people in affected areas. One of these impacts is the potential for increased levels of conflict and accordingly, researchers are examining the relationship between climate variability and conflict. Thus far, there is a lack of consensus on the direction of this relationship. We contribute to the climate variability and conflict literature by incorporating Markov transitional probabilities into panel logit models to analyze how monthly deviations in rainfall affect the likelihood that a grid cell transitions to an above average level of conflict in Sub-Saharan Africa. To control for differences in seasons across the continent, we model this relationship for each of the regions of Sub-Saharan Africa separately - East, Central, West, and Southern. We find significant seasonal and regional effects between rainfall and the probability that a grid cell transitions from a state of peace to a state of conflict. In particular, above average rainfall is associated with a higher likelihood of transitioning into conflict during the dry season. Further, each region has specific months-primarily those associated with prime crop harvest periods-where variations in rainfall significantly influence conflict. We also find regional variations in the linkage between rainfall and conflict type related to the types of conflict that predominate in particular regions of Sub- Saharan Africa. These findings are important for policymakers because they suggest additional law enforcement and/or peacekeeping resources may be needed in times of above average rainfall. Policies that provide financial support for farmers or other sectors, such as mining, that are impacted by rainfall patterns may also be a useful strategy for conflict mitigation.

A broadband integrated time series (BITS) for longitudinal analyses of the digital divide.

To evaluate actions taken to implement the Telecommunications Act of 1996, the primary goal of which was to foster competition in the industry, the FCC created a standardized form (Form 477) to collect information about broadband deployment and competition in local telephone service. These data represent the best publicly available record of broadband provision in the United States. Despite the potential benefits offered by this database, there are several nuances to these data related to shifting geographies and reporting requirements that uncorrected, prevent them from being used as an uninterrupted time series for longitudinal analyses. Given the analytical challenges associated with the FCC Form 477 data, the purpose of this paper is to present a solution to the fragmented nature of these data which prevents meaningful longitudinal analyses of the digital divide. Specifically, this paper develops and describes a procedure for producing an integrated broadband time series (BITS) for the last decade (2008-2018). This includes the procedures for using these data, their value to social and economic analysis, and their underlying limitations. The core contribution of this paper is the creation of data infrastructure for investigating the evolution of the digital divide.

Exploring the Sensitivity of Choropleths under Attribute Uncertainty.

The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.

Correction: A Burgeoning Crisis? A Nationwide Assessment of the Geography of Water Affordability in the United States.

[This corrects the article DOI: 10.1371/journal.pone.0169488.].

A Burgeoning Crisis? A Nationwide Assessment of the Geography of Water Affordability in the United States.

While basic access to clean water is critical, another important issue is the affordability of water access for people around the globe. Prior international work has highlighted that a large proportion of consumers could not afford water if priced at full cost recovery levels. Given growing concern about affordability issues due to rising water rates, and a comparative lack of work on affordability in the developed world, as compared to the developing world, more work is needed in developed countries to understand the extent of this issue in terms of the number of households and persons impacted. To address this need, this paper assesses potential affordability issues for households in the United States using the U.S. EPA's 4.5% affordability criteria for combined water and wastewater services. Analytical results from this paper highlight high-risk and at-risk households for water poverty or unaffordable water services. Many of these households are clustered in pockets of water poverty within counties, which is a concern for individual utility providers servicing a large proportion of customers with a financial inability to pay for water services. Results also highlight that while water rates remain comparatively affordable for many U.S. households, this trend will not continue in the future. If water rates rise at projected amounts over the next five years, conservative projections estimate that the percentage of U.S. households who will find water bills unaffordable could triple from 11.9% to 35.6%. This is a concern due to the cascading economic impacts associated with widespread affordability issues; these issues mean that utility providers could have fewer customers over which to spread the large fixed costs of water service. Unaffordable water bills also impact customers for whom water services are affordable via higher water rates to recover the costs of services that go unpaid by lower income households.