scholarly journals Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

2019 ◽  
Vol 16 (20) ◽  
pp. 4012
Author(s):  
Yang ◽  
Yao ◽  
Chen ◽  
Ye ◽  
Jin
Keyword(s):  
High Resolution ◽  
Total Population ◽  
Population Data ◽  
Coastal Hazards ◽  
Population Exposure ◽  
Coastal Zones ◽  
Point Of Interest ◽  
Population Grid ◽  
Populous Country ◽  
Coastal China

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

scholarly journals Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter

2021 ◽  
Vol 13 (12) ◽  
pp. 5747-5801
Author(s):  
Kytt MacManus ◽  
Deborah Balk ◽  
Hasim Engin ◽  
Gordon McGranahan ◽  
Rya Inman
Keyword(s):  
Urban Areas ◽  
Urban Population ◽  
Population Data ◽  
Data Sources ◽  
Coastal Hazards ◽  
Sensitivity Analyses ◽  
Future Research ◽  
Accurate Estimation ◽  
Coastal Zones ◽  
Urban Rural

Abstract. The accurate estimation of population living in the low-elevation coastal zone (LECZ) – and at heightened risk from sea level rise – is critically important for policymakers and risk managers worldwide. This characterization of potential exposure depends on robust representations not only of coastal elevation and spatial population data but also of settlements along the urban–rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world's population – and an even greater share of the urban population – lived in the LECZ (McGranahan et al., 2007a). Those estimates were constrained in several ways, not only most notably by a single 10 m LECZ but also by a dichotomous urban–rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million and nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g., the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population and urban-proxy data sources in order to guide future research and improvements to characterizing risk in low-elevation coastal zones (https://doi.org/10.7927/d1x1-d702, CIESIN and CIDR, 2021).

scholarly journals Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990–2015: How data choices matter

2021 ◽  
Author(s):  
Kytt MacManus ◽  
Deborah Balk ◽  
Hasim Engin ◽  
Gordon McGranahan ◽  
Rya Inman
Keyword(s):  
Urban Areas ◽  
Urban Population ◽  
Population Data ◽  
Data Sources ◽  
Coastal Hazards ◽  
Sensitivity Analyses ◽  
Future Research ◽  
Accurate Estimation ◽  
Coastal Zones ◽  
Urban Rural

Abstract. The accurate estimation of population living in the Low Elevation Coastal Zone (LECZ), and at heightened risk from sea level rise, is critically important for policy makers and risk managers worldwide. This characterization of potential exposure depends not only on robust representations of coastal elevation and spatial population data, but also of settlements along the urban-rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world’s population, and an even greater share of the urban population, lived in the LECZ (McGranahan et al., 2007). Those estimates were constrained in several ways, most notably by a single 10-meter LECZ, but also by a dichotomous urban-rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million to nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g. the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers, and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population, and urban proxy data sources in order to guide future research and improvements to characterizing risk in low elevation coastal zones. DOI: assigned upon completion of data peer-review.

Faktor yang Berhubungan dengan Penggunaan Partograf dalam Persalinan oleh Bidan Praktik Mandiri

2020 ◽  
Vol 2 (2) ◽  
pp. 72-80
Author(s):  
Niluh Nita Silfia
Keyword(s):  
Total Population ◽  
Sampling Technique ◽  
Population Data ◽  
Statistical Test ◽  
Influential Factor ◽  
Test Results ◽  
Cross Sectional ◽  
Factors Associated ◽  
Survey Results ◽  
Research Criteria

Partographs are guidelines for childbirth observations that will facilitate labor assistants in first identifying emergency cases and complications for mothers and fetuses. Preliminary survey at the Sigi Community Health Sub-Center (Pustu) of the 8 Pustu midwives found two midwives (25%) to complete a complete partograph, six midwives (75%) incomplete. The purpose of this study was to determine the determinant factors associated with the use of partographs in labor. The design of this study used observational analytic methods with a cross-sectional approach. 24 BPM survey results were obtained with 30 samples of midwives who met the research criteria and data completeness. The sampling technique was by the total population. Data analysis used logistic regression. The multivariate analysis results showed that APN training was the most influential factor in the use of partographs in labor by midwives. Statistical test results obtained a POR value of 37.7 (95% CI 12.1 - 60.2). This study suggests that midwives must have APN certificates to be valid in providing services.

scholarly journals The implementation of a mobile application to support community-based health workers

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A C F Martins ◽  
P L Pereira ◽  
A C C N Mafra ◽  
J L Miraglia ◽  
C N Monteiro ◽  
...  
Keyword(s):  
Health Care ◽  
Mobile Application ◽  
Total Population ◽  
Health Workers ◽  
Data Entry ◽  
Population Data ◽  
Administrative Staff ◽  
Economic Data ◽  
Process Change ◽  
Community Based

Abstract Issue Real-time access to up-to-date population information is essential to the strategic planning of primary health care (PHC). In the Brazilian public health system community-based health workers (CHWs), working as part of PHC teams, collect health, demographic and socio-economic data from individuals mainly through paper-based forms that are later entered manually into electronic information systems. Mobile applications could help to improve the quality and speed of this process facilitating the CHWs day-to-day work while improving the access to the collected information. Description of the Problem During September of 2019, a mobile application installed in tablets for the collection of health, demographic and socio-economic data was deployed for 432 CHWs of 87 PHC teams in the southern region of São Paulo, Brazil, serving a total population of 283,324 individuals. During implementation, the acceptability and challenges faced by CHWs were evaluated and the time taken to complete data entry. Results Seventy-two hours of training were offered to CHWs and other 139 professionals including managers, nurses and administrative staff (AS). Some CHWs reported concerns about the process change and fear of not being able to operate the application, especially those unfamiliar to the technology. With training and team support, fear was transformed into satisfaction with the realization of the benefits of the system. The main infrastructure challenge was the need for installation of Wi-Fi routers in some health care units, in addition to the replacement 4.4% of damaged tablets. In four months 97.6% of the total population was registered in the application. Lessons A WhatsApp group was created to clarify AS doubts, receive suggestions and disseminate guidelines. AS remained as the reference point at healthcare units and data completeness still needs to be reinforced. Key messages A mobile application was well-accepted by CHWs and could facilitate the collection of population data. A tablet app proved to be a useful tool to generate information for the PHC teams.

scholarly journals Population Exposure to Ambient PM2.5 at the Subdistrict Level in China

2018 ◽  
Vol 15 (12) ◽  
pp. 2683 ◽  
Author(s):  
Ying Long ◽  
Jianghao Wang ◽  
Kang Wu ◽  
Junjie Zhang
Keyword(s):  
Air Pollution ◽  
Action Plan ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
World Health ◽  
Health Concern ◽  
Population Exposure ◽  
Air Pollution Control ◽  
Annual Average ◽  
Potential Population

Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM2.5 requires high-resolution pollution and population information. This paper assesses China’s potential population exposure to PM2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China’s population-weighted annual average PM2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM2.5 at levels above WHO’s daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM2.5 above China’s daily standard for longer than half a year will be reduced by 85%.

Metrics for the comparative analysis of geospatial datasets with applications to high-resolution grid-based population data

GeoJournal ◽  
2007 ◽  
Vol 69 (1-2) ◽  
pp. 81-91 ◽  
Author(s):  
Aarthy Sabesan ◽  
Kathleen Abercrombie ◽  
Auroop R. Ganguly ◽  
Budhendra Bhaduri ◽  
Eddie A. Bright ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
High Resolution ◽  
Population Data ◽  
Geospatial Datasets ◽  
Grid Based

scholarly journals Quantifying the Health Burden Misclassification from the Use of Different PM2.5 Exposure Tier Models: A Case Study of London

2020 ◽  
Vol 17 (3) ◽  
pp. 1099
Author(s):  
Vasilis Kazakos ◽  
Zhiwen Luo ◽  
Ian Ewart
Keyword(s):  
Urban Areas ◽  
Total Population ◽  
Total Mortality ◽  
Population Exposure ◽  
Exposure Concentration ◽  
Time Activity ◽  
Health Burden ◽  
Mortality Burden ◽  
London Underground

Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration (i.e., approximately 37%) and as a result the largest change in mortality (i.e., health burden/mortality misclassification) was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174–1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments.

scholarly journals Racial Dot Maps Based on Dasymetrically Modeled Gridded Population Data

2019 ◽  
Vol 8 (5) ◽  
pp. 157 ◽  
Author(s):  
Anna Dmowska ◽  
Tomasz F. Stepinski
Keyword(s):  
United States ◽  
Visual Analysis ◽  
Census Data ◽  
Population Data ◽  
Random Order ◽  
The United States ◽  
Racial Groups ◽  
Grid Data ◽  
Population Grid ◽  
Grid Based

Racial geography, mapping spatial distributions of different racial groups, is of keen interest in a multiracial society like the United States. A racial dot map is a method of visualizing racial geography, which depicts spatial distribution, population density, and racial mix in a single, easy-to-understand map. Because of the richness of information it carries, the dot map is an excellent tool for visual analysis of racial distribution. Presently-used racial dot maps are based on the Census data at the tract or the block level. In this paper, we present a method of constructing a more spatially-accurate racial dot map based on a sub-block-resolution population grid. The utility of our dot maps is further enhanced by placing dots on the map in random order regardless of the race they represent in order to achieve a more accurate depiction of local racial composition. We present a series of comparisons between dot maps based on tract, block, and grid data. The advantage of a grid-based dot map is evident from the visual comparison of all maps with an actual image of the mapped area. We make available the R code for constructing grid-based dot maps. We also make available 2010 grid-based racial dot maps for all counties in the conterminous United States.

scholarly journals Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany

2020 ◽  
Vol 12 (10) ◽  
pp. 3976 ◽  
Author(s):  
Sebastian Eichhorn
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Population Distribution ◽  
Population Data ◽  
Added Value ◽  
Moran’S I ◽  
Dispersion Measure ◽  
Test Statistics ◽  
Moran's I ◽  
Urban Density

High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation.

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