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AbstractGeospatial analyses of human-environment interactions are challenged by the multi-scale, multi-dimensional nature of human-environment systems. Research in such contexts must often rely on integrating multiple, independently produced data sources, which presents heterogenous data qualities and interoperability challenges. Understanding data quality and transparency becomes increasingly important in these contexts, and multi‐granularity and context specific spatial data quality indicators are needed. We develop a data pedigree system that accounts for multiple data quality aspects, geospatial ambiguities that may hinder interoperability, and the fitness-for-use of each data source for indicating causal linkages between human activities and environmental change. We demonstrate its application to a particularly challenging and data sparse case study of identifying the location and timing of transnational cocaine trafficking, or ‘narco-trafficking’, in Central America with five spatial and temporal data quality indicators: geographic clarity, geographic interpretation, provenance, temporal specificity, and narco-trafficking certainty. The proposed data pedigree system provides a systematic and coherent analytical framework for interoperability, comparison, and corroboration of fragmented and incomplete data, which are needed to support advanced geospatial analyses, such as causal inference techniques. The study demonstrates the transferability and operationalization of the data pedigree system for examining complex human-environment interactions, especially those influenced by illicit economies. [ABSTRACT FROM AUTHOR]

Ensuring food security requires food production and distribution systems function throughout disruptions. Understanding the factors that contribute to the global food system's ability to respond and adapt to such disruptions (i.e. resilience) is critical for understanding the long-term sustainability of human populations. Variable impacts of production shocks on food supply between countries indicate a need for national-scale resilience indicators that can provide global comparisons. However, methods for tracking changes in resilience have had limited application to food systems. We developed an indicator-based analysis of food systems resilience for the years 1992-2011. Our approach is based on three dimensions of resilience: socio-economic access to food in terms of income of the poorest quintile relative to food prices, biophysical capacity to intensify or extensify food production, and the magnitude and diversity of current domestic food production. The socio-economic indicator has large variability, but with low values concentrated in Africa and Asia. The biophysical capacity indicator is highest in Africa and Eastern Europe, in part because of high potential for extensification of cropland and for yield gap closure in cultivated areas. However, the biophysical capacity indicator has declined globally in recent years. The production diversity indicator has increased slightly, with a relatively even geographic distribution. Few countries had exclusively high or low values for all indicators. Collectively, these results are the basis for global comparisons of resilience between nations, and provide necessary context for developing generalizations about the resilience in the global food system.

Spatially diverse trends in population growth, climate change, industrialization, urbanization and economic development are expected to change future food supply and demand. These changes may affect the suitability of land for food production, implying elevated risks especially for resourceconstrained, food-importing countries.Wepresent the evolution of biophysical redundancy for agricultural production at country level, from 1992 to 2012. Biophysical redundancy, defined as unused biotic and abiotic environmental resources, is represented by the potential food production of 'spare land', available water resources (i.e., not already used for human activities), as well as production increases through yield gap closure on cultivated areas and potential agricultural areas. In 2012, the biophysical redundancy of 75 (48) countries, mainly in North Africa, Western Europe, the Middle East and Asia, was insufficient to produce the caloric nutritional needs for at least 50% (25%) of their population during a year. Biophysical redundancy has decreased in the last two decades in 102 out of 155 countries, 11 of these went from high to limited redundancy, and nine of these from limited to very low redundancy. Although the variability of the drivers of change across different countries is high, improvements in yield and population growth have a clear impact on the decreases of redundancy towards the very low redundancy category.Wetook a more detailed look at countries classified as 'Low Income Economies (LIEs)' since they are particularly vulnerable to domestic or external food supply changes, due to their limited capacity to offset for food supply decreases with higher purchasing power on the international market. Currently, nine LIEs have limited or very low biophysical redundancy. Many of these showed a decrease in redundancy over the last two decades, which is not always linked with improvements in per capita food availability.

Despite more than 40 years of counterdrug interdiction efforts in the Western Hemisphere, cocaine trafficking, or "narco-trafficking", networks continue to evolve and increase their global reach. Counterdrug interdiction continues to fall short of performance targets, due to the adaptability of narco-trafficking networks and spatially complex constraints on interdiction operations (e.g., resources, jurisdictional). Due to these dynamics, current modeling approaches offer limited strategic insights into time-varying, spatially optimal allocation of counterdrug interdiction assets. This study presents coupled agent-based and spatial optimization models to investigate the co-evolution of counterdrug interdiction deployment and narco-trafficking networks' adaptive responses. Increased spatially optimized interdiction assets were found to increase seizure volumes. However, the value per seized shipment concurrently decreased and the number of active nodes increased or was unchanged. Narco-trafficking networks adaptively responded to increased interdiction pressure by spatially diversifying routes and dispersing shipment volumes. Thus, increased interdiction pressure had the unintended effect of expanding the spatial footprint of narco-trafficking networks. This coupled modeling approach enabled the study of narco-trafficking network evolution while being subjected to varying interdiction pressure as a spatially complex adaptive system. Capturing such co-evolution dynamics is essential for simulating traffickers' realistic adaptive responses to a wide range of interdiction scenarios. [ABSTRACT FROM AUTHOR]

Land change models are important tools for land systems analysis, but their potential for causal inference using a counterfactual approach remains underdeveloped. This paper reviews the state of counterfactual land change modeling with the intent of causal inference. All of the reviewed studies promoted the value of creating 'counterfactual worlds' via simulation modeling to untangle complex causation in land systems in order to assess the effects of specific interventions and/or historical events. Several models used counterfactual analysis to challenge prevailing assumptions motivating past policy interventions, while others isolated the spatial heterogeneity of policy effects. The review also highlights methodological limitations and proposes best practices specific to counterfactual land change modeling for causal inference, such as ensemble approaches and multiple calibration-validation iterations. Counterfactual modeling is still underdeveloped in land system science, but it holds promise for advancing causal inference for some of the most challenging to study land change phenomenon. [ABSTRACT FROM AUTHOR]

Disparities in geographic access to medication for opioid use disorder (OUD) are well documented. Further, historical implications of systemic racism and of the longstanding War on Drugs in the United States have driven both social and spatial inequities in access to treatment. This work builds on a previously published spatial access methodology that uses a gravity-based variant of the enhanced two-step floating catchment area model, to determine how OUD pharmacotherapy accessibility varies nationally at the census tract level. Medication for OUD (MOUD) accessibility scores are then analyzed by rurality and racial/ethnic segregation. The Getis-Ord Gi* statistic was used to identify clusters of high or low accessibility to MOUD, while the interaction index was used as a measure of segregation to assess the racial/ethnic population distribution. The results of the clustering statistic, and the associated significance values were then compared to rurality and interaction using the Chi-Square test to determine if hot or cold areas of access are independent of rurality and racial/ethnic spatial distributions. Lastly, the percentage of buprenorphine providers at capacity by census tract was calculated using the binary ‘atcapacity’ data attribute associated with each SAMHSA buprenorphine provider record. The results demonstrate distinct spatial variability of MOUD resources based on urbanicity and racial makeup that have significant implications for intervention or policy reform that seeks to encourage equal access to both treatment modalities. [ABSTRACT FROM AUTHOR]

Population growth and overdevelopment are driving complicated interactions between human and natural systems. A preliminary investigation of human and natural systems as hierarchical, complex systems suggests that problematic, emergent behaviours are the result of 'induced coupling' between the two systems. Human manipulations aimed at mitigating natural disasters often change how human and natural systems interact by linking their behaviours in space and time. The dynamics of each separate system are fundamentally altered when they become coupled. This coupling drives unexpected, emergent behaviours through the introduction of strong, nonlinear feedbacks. Human-landscape interactions present a unique challenge because of the mismatch in scales between coupled processes. Induced coupling describes problematic system behaviours (e.g. natural disasters intensified by human actions) as the product of coupling short-term human activities to long-term natural processes. As a descriptive model, induced coupling draws upon the conceptualization of human and natural systems as hierarchical complex systems, and it utilizes a systems dynamics approach to identify delays introduced by human manipulations. Induced coupling aides in the description and analysis of coupled human-landscape interactions, and provides a focus for numerical modelling and identification of leverage points for management actions. Copyright © 2009 John Wiley & Sons, Ltd. Keywords induced coupling; systems dynamics; hierarchical systems; human-landscape interactions; protective measures, INTRODUCTION A new class of environmental problem has emerged that is far more complex than traditional ones. Population growth and overdevelopment are driving complex interactions between human systems and natural [...]