Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts.

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands.

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species' niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

Examining How Social Risk Factors Are Integrated Into Clinical Settings Using Existing Data: A Scoping Review.

PURPOSE: Integrating social care into clinical care requires substantial resources. Use of existing data through a geographic information system (GIS) has the potential to support efficient and effective integration of social care into clinical settings. We conducted a scoping literature review characterizing its use in primary care settings to identify and address social risk factors. METHODS: In December 2018, we searched 2 databases and extracted structured data for eligible articles that (1) described the use of GIS in clinical settings to identify and/or intervene on social risks, (2) were published between December 2013 and December 2018, and (3) were based in the United States. Additional studies were identified by examining references. RESULTS: Of the 5,574 articles included for review, 18 met study eligibility criteria: 14 (78%) were descriptive studies, 3 (17%) tested an intervention, and 1 (6%) was a theoretical report. All studies used GIS to identify social risks (increase awareness); 3 studies (17%) described interventions to address social risks, primarily by identifying relevant community resources and aligning clinical services to patients' needs. CONCLUSIONS: Most studies describe associations between GIS and population health outcomes; however, there is a paucity of literature regarding GIS use to identify and address social risk factors in clinical settings. GIS technology may assist health systems seeking to address population health outcomes through alignment and advocacy; its current application in clinical care delivery is infrequent and largely limited to referring patients to local community resources.

Geospatial techniques for monitoring and mitigating climate change and its effects on human health.

This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.

Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual's physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google's Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.

Land and Seabed Surface Modelling in the Coastal Zone Using UAV/USV-Based Data Integration.

The coastal zone is an area that includes the sea coast and adjacent parts of the land and sea, where the mutual interaction of these environments is clearly marked. Hence, the modelling of the land and seabed parts of the coastal zone is crucial and necessary in order to determine the dynamic changes taking place in this area. The accurate determination of the terrain in the coastal zone is now possible thanks to the use of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The aim of this article is to present land and seabed surface modelling in the coastal zone using UAV/USV-based data integration. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to a public beach in Gdynia (Poland) in 2022 using the DJI Phantom 4 Real Time Kinematic (RTK) UAV and the AutoDron USV. As a result of geospatial data integration, topo-bathymetric models in the coastal zone were developed using the following terrain-modelling methods: Inverse Distance to a Power (IDP), kriging, Modified Shepard's Method (MSM) and Natural Neighbour Interpolation (NNI). Then, the accuracies of the selected models obtained using the different interpolation methods, taking into account the division into land and seabed parts, were analysed. Research has shown that the most accurate method for modelling both the land and seabed surfaces of the coastal zone is the kriging (linear model) method. The differences between the interpolated and measurement values of the R95 measurement are 0.032 m for the land part and 0.034 m for the seabed part. It should also be noted that the data interpolated by the kriging (linear model) method showed a very good fit to the measurement data recorded by the UAVs and USVs.

Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Multiresolution Analyses of Neighborhood Correlates of Crime: Smaller Is Not Better.

Population analyses of the correlates of neighborhood crime implicitly assume that a single spatial unit can be used to assess neighborhood effects. However, no single spatial unit may be suitable for analyses of the many social determinants of crime. Instead, effects may appear at multiple spatial resolutions, with some determinants acting broadly, others locally, and still others as some function of both global and local conditions. We provide a multiresolution spatial analysis that simultaneously examines US Census block, block group, and tract effects of alcohol outlets and drug markets on violent crimes in Oakland, California, incorporating spatial lag effects at the 2 smaller spatial resolutions. Using call data from the Oakland Police Department from 2010-2015, we examine associations of assaults, burglaries, and robberies with multiple resolutions of alcohol outlet types and compare the performance of single (block-level) models with that of multiresolution models. Multiresolution models performed better than the block models, reflected in improved deviance and Watanabe-Akaike information criteria and well-supported multiresolution associations. By considering multiple spatial scales and spatial lags in a Bayesian framework, researchers can explore multiresolution processes, providing more detailed tests of expectations from theoretical models and leading the way to more effective intervention efforts.

Disparity in HIV Service Interruption in the Outbreak of COVID-19 in South Carolina.

To examine HIV service interruptions during the COIVD-19 outbreak in South Carolina (SC) and identify geospatial and socioeconomic correlates of such interruptions, we collected qualitative, geospatial, and quantitative data from 27 Ryan White HIV clinics in SC in March, 2020. HIV service interruptions were categorized (none, minimal, partial, and complete interruption) and analyzed for geospatial heterogeneity. Nearly 56% of the HIV clinics were partially interrupted and 26% were completely closed. Geospatial heterogeneity of service interruption existed but did not exactly overlap with the geospatial pattern of COVID-19 outbreak. The percentage of uninsured in the service catchment areas was significantly correlated with HIV service interruption (F = 3.987, P = .02). This mixed-method study demonstrated the disparity of HIV service interruptions in the COVID-19 in SC and suggested a contribution of existing socioeconomic gaps to this disparity. These findings may inform the resources allocation and future strategies to respond to public health emergencies.

Every Child on the Map: A Theory of Change Framework for Improving Childhood Immunization Coverage and Equity Using Geospatial Data and Technologies.

The effective use of geospatial data and technologies to collect, manage, analyze, model, and visualize geographic data has great potential to improve data-driven decision-making for immunization programs. This article presents a theory of change for the use of geospatial technologies for immunization programming-a framework to illustrate the ways in which geospatial data and technologies can contribute to improved immunization outcomes and have a positive impact on childhood immunization coverage rates in low- and middle-income countries. The theory of change is the result of a review of the state of the evidence and literature; consultation with implementers, donors, and immunization and geospatial technology experts; and a review of country-level implementation experiences. The framework illustrates how the effective use of geospatial data and technologies can help immunization programs realize improvements in the number of children immunized by producing reliable estimates of target populations, identifying chronically missed settlements and locations with the highest number of zero-dose and under-immunized children, and guiding immunization managers with solutions to optimize resource distribution and location of health services. Through these direct effects on service delivery, geospatial data and technologies can contribute to the strengthening of the overall health system with equity in immunization coverage. Recent implementation of integrated geospatial data and technologies for the immunization program in Myanmar demonstrate the process that countries may experience on the path to achieving lasting systematic improvements. The theory of change presented here may serve as a guide for country program managers, implementers, donors, and other stakeholders to better understand how geospatial tools can support immunization programs and facilitate integrated service planning and equitable delivery through the unifying role of geography and geospatial data.

Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction.

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

Two judging criteria to check validity of a model for filling gaps caused by incomplete geospatial data.

Many models can be used to fill the gaps caused by incomplete geospatial data. But not all are valid. To study the validity of geospatial information diffusion model, in this article, two judging criteria are suggested to check if a model is valid for filling a gap unit. The root mean squared error of a model with a given sample after removing a test point is called datum error of the model. The error between real value and estimated value of the test point is called forecasting error of the model. The first criterion says that, when the average forecasting error is less than the average datum error, the model is invalid. The second criterion says that, the smaller the errors, the more valid the model. The results of computer simulation show that geospatial information diffusion model is more valid than the geographically weighted regression and the back propagation neural network.

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets.

BACKGROUND: Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals' geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. METHODS: This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. RESULTS: The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations. CONCLUSIONS: This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

Exploring the Use of Smartphone Geofencing to Study Characteristics of Alcohol Drinking Locations in High-Risk Gay and Bisexual Men.

BACKGROUND: Geofencing offers new opportunities to study how specific environments affect alcohol use and related behavior. In this study, we examined the feasibility of using geofencing to examine social/environmental factors related to alcohol use and sexual perceptions in a sample of gay and bisexual men (GBM) who engage in heavy drinking and high-risk sex. METHODS: HIV-negative GBM (N = 76) completed ecological momentary assessments for 30 days via a smartphone application and were prompted to complete surveys when inside general geofences set around popular bars and clubs. A subset (N = 45) were also asked to complete surveys when inside personal geofences, which participants set themselves by identifying locations where they typically drank heavily. RESULTS: Approximately 49% of participants received a survey prompted by a general geofence. Among those who identified at least 1 personal drinking location, 62.2% received a personal geofence-prompted survey. Of the 175 total location-based surveys, 40.2% occurred when participants were not at the location that was intended to be captured. Participants reported being most able to openly express themselves at gay bars/clubs and private residences, but these locations were also more "sexualized" than general bars/clubs. Participants did not drink more heavily at gay bars/clubs, but did when in locations with more intoxicated patrons or guests. CONCLUSIONS: Geofencing has the potential to improve the validity of studies exploring environmental influences on drinking. However, the high number of "false-positive" prompts we observed suggests that geofences should be used carefully until improvements in precision are more widely available.

Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data.

Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.

Utilizing geospatial information to implement SDGs and monitor their Progress.

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.

An information theory-based approach to assessing spatial patterns in complex systems.

Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. Methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.

A cross-sectional ecological analysis of international and sub-national health inequalities in commercial geospatial resource availability.

BACKGROUND: Commercial geospatial data resources are frequently used to understand healthcare utilisation. Although there is widespread evidence of a digital divide for other digital resources and infra-structure, it is unclear how commercial geospatial data resources are distributed relative to health need. METHODS: To examine the distribution of commercial geospatial data resources relative to health needs, we assembled coverage and quality metrics for commercial geocoding, neighbourhood characterisation, and travel time calculation resources for 183 countries. We developed a country-level, composite index of commercial geospatial data quality/availability and examined its distribution relative to age-standardised all-cause and cause specific (for three main causes of death) mortality using two inequality metrics, the slope index of inequality and relative concentration index. In two sub-national case studies, we also examined geocoding success rates versus area deprivation by district in Eastern Region, Ghana and Lagos State, Nigeria. RESULTS: Internationally, commercial geospatial data resources were inversely related to all-cause mortality. This relationship was more pronounced when examining mortality due to communicable diseases. Commercial geospatial data resources for calculating patient travel times were more equitably distributed relative to health need than resources for characterising neighbourhoods or geocoding patient addresses. Countries such as South Africa have comparatively high commercial geospatial data availability despite high mortality, whilst countries such as South Korea have comparatively low data availability and low mortality. Sub-nationally, evidence was mixed as to whether geocoding success was lowest in more deprived districts. CONCLUSIONS: To our knowledge, this is the first global analysis of commercial geospatial data resources in relation to health outcomes. In countries such as South Africa where there is high mortality but also comparatively rich commercial geospatial data, these data resources are a potential resource for examining healthcare utilisation that requires further evaluation. In countries such as Sierra Leone where there is high mortality but minimal commercial geospatial data, alternative approaches such as open data use are needed in quantifying patient travel times, geocoding patient addresses, and characterising patients' neighbourhoods.