Spatial accessibility and travel to pharmacy by type in the Detroit region.

BACKGROUND: Community pharmacies are a critical part of the health care provision system. Yet less is understood about the spatial accessibility to pharmacies and how people travel to reach these services. OBJECTIVES: This study compared spatial accessibility and actual travel to different types of pharmacies among selected neighborhoods in the Detroit region. METHODS: Three types of neighborhoods were selected and compared, including two lower income Black urban neighborhoods of high-density and four upper income White suburbs (two of low density and two of high density). Spatial accessibility was computed by pharmacy type and compared among neighborhoods using ANOVA. Pharmacy trips reported in a travel survey were geocoded and linked with community pharmacies in a list generated from ReferenceUSA business data. Destination choices were mapped and the relationship between spatial accessibility and actual distance traveled was examined using ordinary least squares regressions. RESULTS: On average, urban residents in Detroit had higher access to local independent pharmacies (0.74 miles to the nearest one) but relatively lower access to national chains (1.35 miles to the nearest one), which most residents relied on. Urban residents also tended to shop around more for services even among national chains. In fact, they bypassed nearby local independent pharmacies and traveled long distances to use farther pharmacies, primarily national chains. The average trip distance to pharmacy was 2.1 miles for urban residents, but only 1.1 miles and 1.5 miles for residents in high-density suburbs and low-density suburbs, respectively. CONCLUSION: Supposedly good spatial access considering all pharmacies together may mask excessive burden in reaching the pharmacy services needed in low-income minority urban communities, as shown in the case of Detroit. Thus, when mapping pharmacy deserts, it is important to distinguish spatial accessibility among different pharmacy types.

How do farmers adapt to agricultural risks in northern India? An agent-based exploration of alternate theories of decision-making.

Agricultural decision-making processes occur in complex and dynamic environments and are highly contextual. Despite evidence to the contrary, utility maximization is often the implicit theoretical assumption underlying agricultural decision-making processes. This study undertakes an exploratory approach to test alternative theories of human decision-making on the process of agricultural adaptation of farmers in India by synthesizing multiple sources of social and environmental data. We developed an empirical agent-based model (ABM) to simulate past adoption decisions of six agricultural adaptation strategies of 959 farmers in northern India. The model assessed the fit of four major decision-making rules - utility maximization, self-satisficing, social norms, and random choice for farmers differentiated by farm size. Scenario analysis was conducted to test whether (and which) alternative decision-making rules offered a better explanation of the adoption of (which) adaptation strategies. Results demonstrated that the utility-maximizing decision rule had a higher fit for productivity-enhancing adaptation strategies, such as adopting high yield varieties and enhanced fertilizer use, with model performance increasing, generally, with farm size. The adoption of climate tolerant varieties by farmers was most closely guided by self-satisficing and social norms decision-rules, with the model performance, under both scenarios, highest for marginal landholders. Marginal farmers are more likely to use these heuristics to adopt climate tolerant varieties as their decisions may not necessarily be geared towards increasing profit, unlike larger farmers. Social norms had a higher fit for the adoption of climate-related strategies, including enhanced irrigation, with model fit increasing, generally, with farm size. Agricultural policy and extension efforts that incorporate the varied motivations and heuristics of agricultural decision-making, rather than assuming adaptation as a utility maximization exercise, can better design, develop, and disseminate solutions to support the adaptive capacity of farmers.

The dynamics of food shopping behavior: Exploring travel patterns in low-income Detroit neighborhoods experiencing extreme disinvestment using agent-based modeling.

Only a handful of studies have leveraged agent-based models (ABMs) to examine public health outcomes and policy interventions associated with uneven urban food environments. While providing keen insights about the role of ABMs in studying urban food environments, these studies underutilize real-world data on individual behavior in their models. This study provides a unique contribution to the ABM and food access literature by utilizing survey data to develop an empirically-rich spatially-explicit ABM of food access. This model is used to simulate and scrutinize individual travel behavior associated with accessing food in low-income neighborhoods experiencing disinvestment in Detroit (Michigan), U.S. In particular, the relationship between trip frequencies, mode of travel, store choice, and distances traveled among individuals grouped into strata based on selected sociodemographic characteristics, including household income and age, is examined. Results reveal a diversified picture of not only how income and age shape food shopping travel but also the different thresholds of tolerance for non-motorized travel to stores. Younger and poorer population subgroups have a higher propensity to utilize non-motorized travel for shopping than older and wealthier subgroups. While all groups tend to travel considerable distances outside their immediate local food environment, different sociodemographic groups maintain unique spatial patterns of grocery-shopping behavior throughout the city and the suburbs. Overall, these results challenge foundational tenets in urban planning and design, regarding the specific characteristics necessary in the built environment to facilitate accessibility to urban amenities, such as grocery stores. In neighborhoods experiencing disinvestment, sociodemographic conditions play a more important role than the built environment in shaping food accessibility and ultimately travel behavior.

A Pilot Study of Pokémon Go and Players' Physical Activity.

OBJECTIVE: Pokémon Go is a mobile game released in 2016 that gained great popularity. The goals of this pilot study were to investigate player's game-related behavior pattern, to evaluate Pokémon Go's impact on players' physical activity (PA) and game enjoyment, and to examine the influence of neighborhood environment on game behavior. MATERIALS AND METHODS: Forty-seven valid online surveys were collected. Participants were asked questions from five aspects regarding their (1) game status, (2) demographic background and pre-game physical activity, (3) game enjoyment and socializing motivations, (4) perceived game impact on their post-game physical activity, and (5) neighborhood environment's influence on their choice of game location. We examined the first four aspects through descriptive statistics and t-tests, and we investigated the neighborhood impact using logistic regression. RESULTS: Sixty-four percent of participants felt that Pokémon Go made them exercise more than before, about three more times, 3 additional hours, and 5.6 extra miles of PA in total per week. This impact did not vary by gender or body weight status. However, 78.7% participants started to quit or reduce game time by the time of the survey. We also found that players' choice of playing Pokémon Go in the neighborhood is positively associated with the perceived safety level and the walk score of their neighborhood, but negatively associated with the number of Pokéstops near home. CONCLUSIONS: Pokémon Go as a location-based mobile game is a promising tool for promoting PA, but more research is needed to prolong its positive impact.

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome.

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances - in terms of model complexity, model evaluation, and model structure - can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from 'yet another model' to doing better science with models.

The Volatility of Data Space: Topology Oriented Sensitivity Analysis.

Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data.

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.