Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling

The estimation of the vertical components of built-up areas from free Digital Elevation Model (DEM) global data filtered by multi-scale convolutional, morphological and textural transforms are generalized at the spatial resolution of 250 meters using linear least-squares regression techniques. Six test cases were selected: Hong Kong, London, New York, San Francisco, Sao Paulo, and Toronto. Five global DEM and two DEM composites are evaluated in terms of 60 combinations of linear, morphological and textural filtering and different generalization techniques. Four generalized vertical components estimates of built-up areas are introduced: the Average Gross Building Height (AGBH), the Average Net Building Height (ANBH), the Standard Deviation of Gross Building Height (SGBH), and the Standard Deviation of Net Building Height (SNBH). The study shows that the best estimation of the net GVC of built-up areas given by the ANBH and SNBH, always contains a greater error than their corresponding gross GVC estimation given by the AGBH and SGBH, both in terms of mean and standard deviation. Among the sources evaluated in this study, the best DEM source for estimating the GVC of built-up areas with univariate linear regression techniques is a composite of the 1-arcsec Shuttle Radar Topography Mission (SRTM30) and the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) using the union operator (CMP_SRTM30-AW3D30_U). A multivariate linear model was developed using 16 satellite features extracted from the CMP_SRTM30-AW3D30_U enriched by other land cover sources, to estimate the gross GVC. A RMSE of 2.40 m and 3.25 m was obtained for the AGBH and the SGBH, respectively. A similar multivariate linear model was developed to estimate the net GVC. A RMSE of 6.63 m and 4.38 m was obtained for the ANBH and the SNBH, respectively. The main limiting factors on the use of the available global DEMs for estimating the GVC of built-up areas are two. First, the horizontal resolution of these sources (circa 30 and 90 meters) corresponds to a sampling size that is larger than the expected average horizontal size of built-up structures as detected from nadir-angle Earth Observation (EO) data, producing more reliable estimates for gross vertical components than for net vertical component of built-up areas. Second, post-production processing targeting Digital Terrain Model specifications may purposely filter out the information on the vertical component of built-up areas that are contained in the global DEMs. Under the limitations of the study presented here, these results show a potential for using global DEM sources in order to derive statistically generalized parameters describing the vertical characteristics of built-up areas, at the scale of 250x250 meters. However, estimates need to be evaluated in terms of the specific requirements of target applications such as spatial population modelling, urban morphology, climate studies and so on.

Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls

BACKGROUND AND AIMS

With no approved vaccines for treating COVID-19 as of August 2020, many health systems and governments rely on contact tracing as one of the prevention and containment methods. However, there have been instances when the infected person forgets his/her contact-persons and does not have their contact details. Therefore, this study aimed at analyzing possible opportunities and challenges of integrating emerging technologies into COVID-19 contact tracing.

METHODS

The study applied literature search from Google Scholar, Science Direct, PubMed, Web of Science, IEEE and WHO COVID-19 reports and guidelines analyzed.

RESULTS

While the integration of technology-based contact tracing applications to combat COVID-19 and break transmission chains promise to yield better results, these technologies face challenges such as technical limitations, dealing with asymptomatic individuals, lack of supporting ICT infrastructure and electronic health policy, socio-economic inequalities, deactivation of mobile devices' WIFI, GPS services, interoperability and standardization issues, security risks, privacy issues, political and structural responses, ethical and legal risks, consent and voluntariness, abuse of contact tracing apps, and discrimination.

CONCLUSION

Integrating emerging technologies into COVID-19 contact tracing is seen as a viable option that policymakers, health practitioners and IT technocrats need to seriously consider in mitigating the spread of coronavirus. Further research is also required on how best to improve efficiency and effectiveness in the utilisation of emerging technologies in contact tracing while observing the security and privacy of people in fighting the COVID-19 pandemic.

Feasibility and Validity of Geographically Explicit Ecological Momentary Assessment With Recall-Aided Space-Time Budgets

We employ data from the Adolescent Health and Development in Context Study-a representative sample of urban youth ages 11-17 in and around the Columbus, OH area-to investigate the feasibility and validity of smartphone-based geographically explicit ecological momentary assessment (GEMA). Age, race, household income, familiarity with smartphones, and self-control were associated with missing global positioning systems (GPS) coverage, whereas school day was associated with discordance between percent of time at home based on GPS-only versus recall-aided space-time budget data. Fatigue from protocol compliance increases missing GPS across the week, which results in more discordance. Although some systematic differences were observed, these findings offer evidence that smartphone-based GEMA is a viable method for the collection of activity space data on urban youth.

Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research

BACKGROUND

GPS tracking is increasingly used in health and aging research to objectively and unobtrusively assess individuals' daily-life mobility. However, mobility is a complex concept and its thorough description based on GPS-derived mobility indicators remains challenging.

METHODS

With the aim of reflecting the breadth of aspects incorporated in daily mobility, we propose a conceptual framework to classify GPS-derived mobility indicators based on their characteristic and analytical properties for application in health and aging research. In order to demonstrate how the classification framework can be applied, existing mobility indicators as used in existing studies are classified according to the proposed framework. Then, we propose and compute a set of selected mobility indicators based on real-life GPS data of 95 older adults that reflects diverse aspects of individuals' daily mobility. To explore latent dimensions that underlie the mobility indicators, we conduct a factor analysis.

RESULTS

The proposed framework enables a conceptual classification of mobility indicators based on the characteristic and analytical aspects they reflect. Characteristic aspects inform about the content of the mobility indicator and comprise categories related to space, time, movement scope, and attribute. Analytical aspects inform how a mobility indicator is aggregated with respect to temporal scale and statistical property. The proposed categories complement existing studies that often underrepresent mobility indicators involving timing, temporal distributions, and stop-move segmentations of movements. The factor analysis uncovers the following six dimensions required to obtain a comprehensive view of an older adult's daily mobility: extent of life space, quantity of out-of-home activities, time spent in active transport modes, stability of life space, elongation of life space, and timing of mobility.

CONCLUSION

This research advocates incorporating GPS-based mobility indicators that reflect the multi-dimensional nature of individuals' daily mobility in future health- and aging-related research. This will foster a better understanding of what aspects of mobility are key to healthy aging.

An overview of GeoAI applications in health and healthcare

The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed.

Employment of GIS techniques to assess the long-term impact of tillage on the soil organic carbon of agricultural fields under hyper-arid conditions

A study on six 50 ha agricultural fields was conducted to investigate the effect of conservation tillage practices on the long-term (1990-2016) changes in the soil organic carbon (SOC) content of the topsoil layers (0-10 cm) of agricultural fields. The experimental fields were selected from the 49 fields of the Tawdeehiya Arable Farm (TAF), located 200 kilometers southeast of Riyadh, the capital city of the Kingdom of Saudi Arabia. Data sets from laboratory determined SOC and the corresponding Landsat images generated vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), were utilized for the prediction of SOC using multivariate regression techniques. Long-term changes in the SOC content of the experimental fields, as a result of different tillage practices, were also studied. The developed SOC prediction models exhibited high accuracy indicated by R2 values ranging from 0.73 to 0.85, RMSE values of 0.34 to 0.85 g kg-1 and P-values of less than 0.0001. The cross-validation results (R2 of 0.61-0.70, RMSE value of 0.34-0.85 g kg-1 and P-values of less than 0.0001) confirmed the high accuracy of the developed SOC prediction models. Results also revealed that the change in the SOC content was clearly associated with soil tillage practices. On the average, 76% of the all agricultural fields in the experimental farm showed a decrease of up to 24 g kg-1 in their SOC content after 10 years (1990-2000) of continuous conventional tillage practices. On the other hand, an average increase of up to 37 g kg-1 in the SOC content was observed in 88% of the studied fields at the end of the study period (2016), where conservation tillage was a continous and consistent practice in the experimental farm.

Geolocator deployment reduces return rate, alters selection, and impacts demography in a small songbird

In the past few years, miniature light-level geolocators have been developed for tracking wild bird species that were previously too small to track during their full annual cycle. Geolocators offer an exciting opportunity to study the full annual cycle for many species. However, the potential detrimental effects of carrying geolocators are still poorly understood, especially for small-bodied birds. Here, we deployed light-level geolocators on common yellowthroat warblers (Geothlypis trichas). Over two years, we monitored return rates and neighborhood demography for 40 warblers carrying a geolocator and 20 reference birds that did not carry a geolocator. We compared the two groups with long-term data from 108 unmanipulated birds breeding at the same location in previous and subsequent years. Overall, we found that individuals carrying a geolocator were less likely to return to the study site in the following year (21% to 33% returned, depending on inclusion criteria) than either contemporaneous controls (55%) or long-term controls (55%). Among birds marked with geolocators, we also detected viability selection for greater wing length, whereas this pattern was not present in control birds. Finally, in each year after geolocator deployment, inexperienced breeders colonized vacant territories and this demographic effect persisted for two years after deployment. Sexual selection and ornamentation are strongly age-dependent in this system, and behavioral data collected after geolocator deployment is likely to differ systematically from natural conditions. Clearly geolocators will continue to be useful tools, but we suggest that future studies should carefully consider the potential for biased returns and the ecological validity of behavioral data collected from geolocator marked populations.

[Analysis of Distribution of Vector-Borne Diseases Using Geographic Information Systems]

The distribution of vector-borne diseases is changing on a global scale owing to issues involving natural environments, socioeconomic conditions and border disputes among others. Geographic information systems (GIS) provide an important method of establishing a prompt and precise understanding of local data on disease outbreaks, from which disease eradication programs can be established. Having first defined GIS as a combination of GPS, RS and GIS, we showed the processes through which these technologies were being introduced into our research. GIS-derived geographical information attributes were interpreted in terms of point, area, line, spatial epidemiology, risk and development for generating the vector dynamic models associated with the spread of the disease. The need for interdisciplinary scientific and administrative collaboration in the use of GIS to control infectious diseases is highly warranted.

Does substance use moderate the association of neighborhood disadvantage with perceived stress and safety in the activity spaces of urban youth?

BACKGROUND

This study investigates the association of activity space-based exposure to neighborhood disadvantage with momentary perceived stress and safety, and the moderation of substance use on those associations, among a sample of 139 urban, primarily African American, adolescents.

METHOD

Geospatial technologies are integrated with Ecological Momentary Assessment (EMA) to capture exposure to neighborhood disadvantage and perceived stress and safety in the activity space. A relative neighborhood disadvantage measure for each subject is calculated by conditioning the neighborhood disadvantage observed at the EMA location on that of the home neighborhood. Generalized estimating equations (GEE) are used to model the effect of relative neighborhood disadvantage on momentary perceived stress and safety, and the extent to which substance use moderates those associations.

RESULTS

Relative neighborhood disadvantage is significantly associated with higher perceived stress, lower perceived safety, and greater substance use involvement. The association of relative neighborhood disadvantage with stress is significantly stronger among those with greater substance use involvement.

CONCLUSION

This research highlights the value of integrating geospatial technologies with EMA and developing personalized measures of environmental exposure for investigating neighborhood effects on substance use, and suggests substance use intervention strategies aimed at neighborhood conditions. Future research should seek to disentangle the causal pathways of influence and selection that relate neighborhood environment, stress, and substance use, while also accounting for the role of gender and family and peer social contexts.

Role of spatial tools in public health policymaking of Bangladesh: opportunities and challenges

In spite of the increasing efforts to gather spatial data in developing countries, the use of maps is mostly for visualization of health indicators rather than informed decision-making. Various spatial tools can aid policymakers to allocate resources effectively, predict patterns in communicable or infectious diseases, and provide insights into geographical factors which are associated with utilization or adequacy of health services. In Bangladesh, the launch of District Health Information System 2, along with recent efforts to gather spatial data of facilities location, provides an interesting opportunity to study the current landscape and the potential barriers in advancing the use of spatial tools for informed decision making. This study assessed the current level of map usage and spatial tools for health sector planning in Bangladesh, focusing on investigating why map usage and spatial tools remained at a basic level for the purpose of health policy. The study design involved in-depth interviews, followed by an expert survey (n = 39) obtained through snowball sampling.Our survey revealed that assessing areas with shortage of community health workers emerged as the top most for basic map usage or primarily for visualization purpose, while planning for emergency and obstetric care services, and disease mapping was the most frequent category for intermediate and advanced map usage, respectively. Furthermore, we found lack of inter-institutional collaboration, lack of continuous availability of trained personnel, and lack of awareness on the use of geographic information system (GIS) as a decision-making tool as three most critical barriers in the current landscape. Our findings highlight the barriers in increasing the adoption of spatial tools for health policymaking and planning in Bangladesh.

Mapping global cropland and field size

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

The growing role of web-based geospatial technology in disaster response and support

This paper examines changes in disaster response and relief efforts and recent web-based geospatial technological developments through an evaluation of the experiences of the Center for Geographic Analysis, Harvard University, of the Sichuan (2008) and Haiti (2010) earthquake responses. This paper outlines how conventional GIS (geographic information systems) disaster responses by governmental agencies and relief response organisations and the means for geospatial data-sharing have been transformed into a more dynamic, more transparent, and decentralised form with a wide participation. It begins by reviewing briefly at historical changes in the employment of geospatial technologies in major devastating disasters, including the Sichuan and Haiti earthquakes (case studies for our geospatial portal project). It goes on to assess changes in the available dataset type and in geospatial disaster responders, as well as the impact of geospatial technological changes on disaster relief effort. Finally, the paper discusses lessons learned from recent responses and offers some thoughts for future development.

Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States

The northern Great Plains (NGP) of the United States has been a hotspot of West Nile virus (WNV) incidence since 2002. Mosquito ecology and the transmission of vector-borne disease are influenced by multiple environmental factors, and climatic variability is an important driver of inter-annual variation in WNV transmission risk. This study applied multiple environmental predictors including land surface temperature (LST), the normalized difference vegetation index (NDVI) and actual evapotranspiration (ETa) derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) products to establish prediction models for WNV risk in the NGP. These environmental metrics are sensitive to seasonal and inter-annual fluctuations in temperature and precipitation, and are hypothesized to influence mosquito population dynamics and WNV transmission. Non-linear generalized additive models (GAMs) were used to evaluate the influences of deviations of cumulative LST, NDVI, and ETa on inter-annual variations of WNV incidence from 2004–2010. The models were sensitive to the timing of spring green up (measured with NDVI), temperature variability in early spring and summer (measured with LST), and moisture availability from late spring through early summer (measured with ETa), highlighting seasonal changes in the influences of climatic fluctuations on WNV transmission. Predictions based on these variables indicated a low WNV risk across the NGP in 2011, which is concordant with the low case reports in this year. Environmental monitoring using remote-sensed data can contribute to surveillance of WNV risk and prediction of future WNV outbreaks in space and time.

Free and simple GIS as appropriate for health mapping in a low resource setting: a case study in eastern Indonesia

BACKGROUND

Despite the demonstrated utility of GIS for health applications, there are perceived problems in low resource settings: GIS software can be expensive and complex; input data are often of low quality. This study aimed to test the appropriateness of new, inexpensive and simple GIS tools in poorly resourced areas of a developing country. GIS applications were trialled in pilot studies based on mapping of health resources and health indicators at the clinic and district level in the predominantly rural province of Nusa Tenggara Timur in eastern Indonesia. The pilot applications were (i) rapid field collection of health infrastructure data using a GPS enabled PDA, (ii) mapping health indicator data using open source GIS software, and (iii) service availability mapping using a free modelling tool.

RESULTS

Through contextualised training, district and clinic staff acquired skills in spatial analysis and visualisation and, six months after the pilot studies, they were using these skills for advocacy in the planning process, to inform the allocation of some health resources, and to evaluate some public health initiatives.

CONCLUSIONS

We demonstrated that GIS can be a useful and inexpensive tool for the decentralisation of health data analysis to low resource settings through the use of free and simple software, locally relevant training materials and by providing data collection tools to ensure data reliability.

Scaling up malaria control in Africa: an economic and epidemiological assessment

This paper estimates the number of people at risk of contracting malaria in Africa using GIS methods and the disease's epidemiologic characteristics. It then estimates yearly costs of covering the population at risk with the package of interventions (differing by level of malaria endemicity and differing for rural and urban populations) for malaria as recommended by the UN Millennium Project. These projected costs are calculated assuming a ramp-up of coverage to full coverage by 2008, and then projected out through 2015 to give a year-by-year cost of meeting the Millennium Development Goal for reducing the burden of malaria by 75%. We conclude that the cost of comprehensive malaria control for Africa is US $3.0 billion per year on average, or around US $4.02 per African at risk.

Tuberculosis incidence in Portugal: spatiotemporal clustering

BACKGROUND

The statistics of disease clustering is one of the most important tools for epidemiologists to detect and monitor public health disease patterns. Nowadays, tuberculosis (TB)--an infectious disease caused by the Mycobacterium tuberculosis--presents different (development in populations and antibiotics resistance) patterns and specialists are very concerned with it and its association to several other diseases and factors. Each year, tuberculosis kills about three million people in the world. In particular, it is responsible for the death of more than one-third of HIV-infected people, who prove particularly susceptible due to a decline in their immune defences. The purpose of this study is to determine if there are spatiotemporal tuberculosis incidence clusters in continental Portugal. The presented case study is based on the notification of new tuberculosis cases (disease incidence), between 2000 and 2004. In methodological terms, the spatial scan statistic, used to identify spatiotemporal clusters, was improved by including two new approaches: definition of window sizes in the cluster scanning processes considering empirical mean spatial semivariograms and an independent and posterior validation of identified clusters (based on geostatistical simulations).

RESULTS

Continental Portugal is organized in 18 districts with 278 sub-districts. For this case study, the number of new notified cases of TB, per sub-district and per year (2000-2004) was available. TB incidence presents clear spatial patterns: a semivariogram consistent with 40% of nugget effect and 60% of spatial contribution, following an exponential model with a range of 143 kilometres. Temporal semivariograms were not conclusive, as only 5 years of data were available. The spatial and temporal persistence of clusters were analyzed considering different models. Significant high incidence rate space-time clusters were identified in three areas of Portugal (between 2000 and 2004) and a purely temporal cluster was identified covering the whole country, during 2002.

CONCLUSION

In terms of spatiotemporal clustering of tuberculosis disease, the proposed methodology allowed the identification of critical spatiotemporal areas. In Portugal there were 3 critical districts (Porto, Setúbal and Lisbon) with high rates of notified incidences between 2000 and 2004. In methodological terms, semivariogram parameters were successfully applied to define spatiotemporal scan window sizes and shapes (ellipsoidal cylinders), showing very good results and performances in the case study. After defining the clusters, these were authenticated through a validation method, based on geostatistical simulations.

Using GIS-Based Approaches to Support Research on Neurotoxicants and Other Children's Environmental Health Threats

Environmental threats to children's health are complex and multifaceted; consequently, children's environmental health research strives to identify areas of elevated exposure, determine whether particular demographic groups are inequitably exposed, and link exposures to incidence of disease. Many environmental health researchers use geographic information systems (GIS) to ex post display the results of their data collection and analysis. This methodological paper shows some ways by which the ex ante integration of GIS into environmental exposure and epidemiological research can significantly enhance: research design; sampling, recruitment, and retention strategies; data management and analysis; and community translation.