Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Questions (RQs)
- (RQ1) What types of geospatial data are compiled for BGSD to examine public health outcomes?
- (RQ2) How do the existing public health studies using sensing data define BGSD? Is there a clear distinction between ‘big’ geospatial data and geospatial ‘big data’ in use?
- (RQ3) What data sources serve as an SDI of geospatial and health/health-related information for researchers to obtain relevant data?
- (RQ4) To what extent has the concept of health SDI been discussed in practice?
2.2. Search Strategy
2.2.1. Inclusion and Exclusion Criteria
2.2.2. Quality Assessment
3. Results
3.1. Journal Categories
3.2. Study Areas
3.3. Study Topics
3.4. Patterns of Data Compilation
3.5. Sources of Data
4. Discussion
4.1. Strengths
4.1.1. BGSD for Assessing the Environments
4.1.2. New Types of Data for BGSD
4.1.3. New Methods for BGSD
4.1.4. Variety of Research Topics with BGSD
4.2. Areas for Improvement and Suggestions
4.2.1. ‘Big’ Geospatial Data vs. Geospatial ‘Big Data’
4.2.2. Limited Areas of Research
4.2.3. Toward Overcoming Ecological Fallacy
4.2.4. Suggestions for Future SDIs
4.3. Strengths and Limitations of This Review
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Journal Categories | Number of Works |
---|---|
Geography (general, remote sensing, geoscience) [12,13,14,15,16,17,18,19,20,21] | 10 |
Public health [22,23,24,25,26,27,28,29,30,31] | 10 |
Environment (physical, built environment) [32,33,34,35,36,37,38,39] | 8 |
Science (computer, engineering, multidisciplinary) [40,41,42,43] | 4 |
Total | 32 |
Regions | Countries | Number of Works |
---|---|---|
Africa | Ethiopia [22] | 1 |
Malawi [12] | 1 | |
Asia | China [13,14,15,23,24,25,26,27,28,29,30,33,34,35,36,37] | 16 |
India [41] | 1 | |
Indonesia [31] | 1 | |
Europe | Denmark [38] | 1 |
Germany [40] | 1 | |
Portugal [39] | 1 | |
North America | USA [16,18,19,42,43] | 5 |
Canada [20] | 1 | |
Global | Multiple countries [17,21,32] | 3 |
Total | 32 |
Topics | Sub-Topics | Number of Works |
---|---|---|
Environments | Livability [15], green space [13,14], night lights [19], noise exposure [39,40], land use [16], park visits [14], water points [12], indoor/outdoor air pollutants [17,20,23,24,29,33,36,39], energy expenditure [39], NDVI [38], mountain green cover [32], low-elevation coastal zones [28], anthropogenic heat emissions [34], socioeconomic factors [29] | 23 |
Vector-borne diseases | Malaria [21,31,41], hemorrhagic fever with renal syndrome [25,26,27], soil-transmitted helminth [22], human rabies [30] | 8 |
Non-vector-borne diseases | COVID-19 [18], Acute respiratory infection [23,24] chronic obstructive pulmonary disease [24], hospital emergency room visits for respiratory diseases [35], upper respiratory tract infection [37], physical activity [42], sleep duration and quality [43], life expectancy [17] | 9 |
Total | 40 * |
Data Type 1 | Data Type 2 | Number of Works |
---|---|---|
Remote sensing | + Other remote sensing data [13,16,20,28,32,34,38] | 7 |
+ Socioeconomic data [19,29,33] | 3 | |
+ Clinical records (individual-level) [21,22,23,24,25,26,27,29,30,31,35,36,37,41] | 13 | |
+ Health statistics (aggregated at a local area) [17] | 1 | |
+ Points of interest (POIs) [15] | 1 | |
+ Social media [14,18,19,33,36] | 5 | |
+ Mobile phone (sensor, location) [39,40] | 2 | |
+ VGI/PGIS [21] | 1 | |
+ UAV [12] | 1 | |
Mobile phone app-based sensing | + GPS data [42] | 1 |
Wearable devices | + Mobile phone (sensor, location) [43] | 1 |
Total | 36 * |
Category | Types | Source Examples | Public Accessibility |
---|---|---|---|
Geospatial | Fully open data [13,15,16,17,20,21,22,28,32] | NASA, OpenStreetMap, Earth Engine, VGI/PGIS | Yes |
Public data [33,34] | National and/or municipal governments | Special permission may be required. | |
Data collected by ‘tech’ companies or from the Internet [14,15,18,19] | Geotagged social media data, POIs | Additional data processing using API or special permission may be required. | |
Data collected from personal devices [12,21] | Personal location data, UAV images | No | |
Health/ health-related | Fully open data [17] | Area-level vital statistics | Yes |
Public data [21,22,23,24,25,26,27,29,30,31,35,36,37,41] | Clinical data | Special permission may be required. | |
Data collected from personal devices [39,40,42,43] | Health-related behaviors (e.g., sleep quality, physical activity) | No | |
Population or socioeconomic | Fully open data [19,29,33] | Census data, public survey, WorldPop | Yes |
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Koh, K.; Hyder, A.; Karale, Y.; Kamel Boulos, M.N. Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. Remote Sens. 2022, 14, 2996. https://doi.org/10.3390/rs14132996
Koh K, Hyder A, Karale Y, Kamel Boulos MN. Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. Remote Sensing. 2022; 14(13):2996. https://doi.org/10.3390/rs14132996
Chicago/Turabian StyleKoh, Keumseok, Ayaz Hyder, Yogita Karale, and Maged N. Kamel Boulos. 2022. "Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health" Remote Sensing 14, no. 13: 2996. https://doi.org/10.3390/rs14132996
APA StyleKoh, K., Hyder, A., Karale, Y., & Kamel Boulos, M. N. (2022). Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. Remote Sensing, 14(13), 2996. https://doi.org/10.3390/rs14132996