Geographic Information Science and the Analysis of Place and Health

Using EMA to explore the role of Black adolescents' experiences in activity spaces in momentary negative emotion and marijuana use

Research examining the role of place in Black adolescents' health behaviors typically examines neighborhoods, with little attention paid to micro geographies such as activity spaces. Understanding experiences in activity spaces may be especially important for Black adolescents living in neighborhoods traditionally characterized as disadvantaged. The SPIN project recruited 75 Black adolescents living in a single neighborhood to complete ecological momentary assessments (EMA) about the activity spaces they encountered over a month. Perceptions of violence and social support in activity spaces in a day are related to marijuana use during the day, relationships partially explained by negative momentary emotions.

River dataset as a potential fluvial transportation network for healthcare access in the Amazon region

Remote areas, such as the Amazon Forest, face unique geographical challenges of transportation-based access to health services. As transportation to healthcare in most of the Amazon Forest is only possible by rivers routes, any travel time and travel distance estimation is limited by the lack of data sources containing rivers as potential transportation routes. Therefore, we developed an approach to convert the geographical representation of roads and rivers in the Amazon into a combined, interoperable, and reusable dataset. To build the dataset, we processed and combined data from three data sources: OpenStreetMap, HydroSHEDS, and GloRiC. The resulting dataset can consider distance metrics using the combination of streets and rivers as a transportation route network for the Amazon Forest. The created dataset followed the guidelines and attributes defined by OpenStreetMap to leverage its reusability and interoperability possibilities. This new data source can be used by policymakers, health authorities, and researchers to perform time-to-care analysis in the International Amazon region.

Prompt-level predictors of compliance in an ecological momentary assessment study of young adults' mental health

BACKGROUND

Ecological momentary assessment (EMA) has become a popular method of gathering information about participants as they go about their daily lives. However, participant non-compliance, especially non-random compliance, in EMA is a concern. Better knowledge of the moment-to-moment factors that predict prompt non-response can inform the design of strategies to mitigate it.

METHOD

We used data from a general population young adult (n = 260) EMA study, 'decades-to-minutes' (D2M) and fitted dynamic structural equation models (DSEMs) to explore a range of candidate momentary predictors of missing the next prompt.

RESULTS

We found that higher levels of stress, overall negative affect, and the specific negative affective state of 'upset' at a given prompt predicted a greater likelihood of missing the next prompt. However, no other specific affective states, alcohol use, experiencing social provocations nor aggressive behaviour predicted missing the next prompt.

LIMITATIONS

The primary limitation of the present study was a lack of information on predictors concurrent with missed prompts.

CONCLUSIONS

Findings point to the potential value of gathering information on momentary negative affect (especially feeling upset) and stress to help inform strategies that intervene to prevent application disengagement at optimal moments and to feed into strategies to mitigate bias due to non-random non-response in EMA studies.

Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai

Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal.

Surveillance of COVID-19 Using Geospatial Data: An Emergency Department Perspective

Background

The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection.

Methods

This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level.

Results

The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density.

Conclusion

This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.

Mapping urban greenspace use from mobile phone GPS data

Urban greenspace is a valuable component of the urban form that has the potential to improve the health and well-being of residents. Most quantitative studies of relationships between health and greenspace to date have investigated associations only with what greenspace exists in the local environment (i.e. provision of greenspace), rather than to what extent it is used. This is due to the difficulty of obtaining usage data in large amounts. In recent years, GPS functionality integrated into mobile phones has provided a potential solution to this problem by making it possible to track which parts of the environment people experience in their day-to-day lives. In this paper, we demonstrate a method to derive cleaned, trip-level information from raw GPS data collected by a mobile phone app, then use this data to investigate the characteristics of trips to urban greenspace by residents of the city of Sheffield, UK. We find that local users of the app spend an average of an hour per week visiting greenspaces, including around seven trips per week and covering a total distance of just over 2.5 km. This may be enough to provide health benefits, but is insufficient to provide maximal benefits. Trip characteristics vary with user demographics: ethnic minority users and users from more socioeconomically deprived areas tend to make shorter trips than White users and those from less deprived areas, while users aged 34 years and over make longer trips than younger users. Women, on average, make more frequent trips than men, as do those who spent more time outside as a child. Our results suggest that most day-to-day greenspace visits are incidental, i.e. travelling through rather than to greenspace, and highlight the importance of including social and cultural factors when investigating who uses and who benefits from urban greenspace.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data

The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

Returning to our roots: The use of geospatial data for nurse-led community research

In the early 20th century, public health nurse, Lillian Wald, addressed the social determinants of health (SDOH) through her work in New York City and her advocacy to improve policy in workplace conditions, education, recreation, and housing. In the early 21st century, addressing the SDOH is a renewed priority and provides nurse researchers with an opportunity to return to our roots. The purpose of this methods paper is to examine how the incorporation of geospatial data and spatial methodologies in community research can enhance the analyses of the complex relationships between social determinants and health. Geospatial technologies, software for mapping and working with geospatial data, statistical methods, and unique considerations are discussed. An exemplar for using geospatial data is presented regarding associations between neighborhood greenspace, neighborhood violence, and children's asthma control. This innovative use of geospatial data illustrates a new frontier in investigating nontraditional connections between the environment and SDOH outcomes.

A Recursive Definition of Goodness of Space for Bridging the Concepts of Space and Place for Sustainability

Conceived and developed by Christopher Alexander through his life’s work, The Nature of Order, wholeness is defined as a mathematical structure of physical space in our surroundings. Yet, there was no mathematics, as Alexander admitted then, that was powerful enough to capture his notion of wholeness. Recently, a mathematical model of wholeness, together with its topological representation, has been developed that is capable of addressing not only why a space is good, but also how much goodness the space has. This paper develops a structural perspective on goodness of space (both large- and small-scale) in order to bridge two basic concepts of space and place through the very concept of wholeness. The wholeness provides a de facto recursive definition of goodness of space from a holistic and organic point of view. A space is good, genuinely and objectively, if its adjacent spaces are good, the larger space to which it belongs is good, and what is contained in the space is also good. Eventually, goodness of space, or sustainability of space, is considered a matter of fact rather than of opinion under the new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living, or more sustainable or less sustainable. Under the new view of space, geography or architecture will become part of complexity science, not only for understanding complexity, but also for making and remaking complex or living structures.

Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions

This research presents a pilot study to develop and compare methods of geographic imputation for estimating the location of missing activity space data collected using geographic ecological momentary assessment (GEMA). As a demonstration, we use data from a previously published analysis of the effect of neighborhood disadvantage, captured at the U.S. Census Bureau tract level, on momentary psychological stress among a sample of 137 urban adolescents. We investigate the impact of listwise deletion on model results and test two geographic imputation techniques adapted for activity space data from hot deck and centroid imputation approaches. Our results indicate that listwise deletion can bias estimates of place effects on health, and that these impacts are mitigated by the use of geographic imputation, particularly regarding inflation of the standard errors. These geographic imputation techniques may be extended in future research by incorporating approaches from the non-spatial imputation literature as well as from conventional geographic imputation and spatial interpolation research that focus on non-activity space data.