scholarly journals A Comparison of the Posterior Choropleth Maps for Disease Mapping

2021 ◽  
Vol 3 (1) ◽  
pp. 47-68
Author(s):  
Balgobin Nandram ◽  
Jie Liu ◽  
Jai Won Choi
Keyword(s):  
Disease Mapping ◽  
Choropleth Maps

scholarly journals Overcoming the misrepresentation of disease burden associated with single aggregation choropleth maps through combining information from many aggregations

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Matthew Tuson ◽  
Matthew Yap ◽  
Mei Ruu Kok ◽  
Bryan Boruff ◽  
Kevin Murray ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Data ◽  
Public Health Surveillance ◽  
Disease Mapping ◽  
Health Surveillance ◽  
Service Planning ◽  
Overlay Method ◽  
Choropleth Maps ◽  
Fine Resolution ◽  
The Impact

BackgroundAccurate disease mapping based on spatiotemporal data is an important aspect of public health surveillance, targeting interventions, and health service planning. This is achieved by public health surveillance organisations around the world through the construction of choropleth maps based on single spatiotemporal aggregations of finer resolution data. However, such maps are undermined by their dependence on the spatiotemporal units used. This dependence is described by the related modifiable areal and temporal unit problems (MAUP; MTUP), also known as change of support problems (COSPs). AimTo accurately map disease. MethodsUsing ischaemic stroke admissions and mental health-related ED presentations in metropolitan Perth between 2013 and 2016 as exemplars, we present a novel zonation overlay approach for disease mapping. This method involves aggregating fine resolution spatial data numerous times instead of just once, using the automated zonation construction software AZTool. Results Through implementing the zonation overlay method in combination with a rolling window of time, both the MAUP and the MTUP may be overcome in the context of disease mapping. Furthermore, the AZTool zonations act as a geographical encryption key, allowing fine resolution, precise maps to be constructed while protecting the privacy of individuals. ConclusionHealth surveillance organisations continue to produce single aggregation choropleth maps of disease, without acknowledging their limitations except in rare cases. Producing such maps and suggesting they should guide policy makers, while being aware of but not acknowledging the impact of COSPs, could be described as scientific malfeasance. However, assuming that most researchers producing such maps are not intending to mislead, we must conclude that COSPs are poorly understood and their impact underestimated. The zonation overlay method we describe can help alleviate the consequences of this continued practice.

scholarly journals Geospatial Analysis of COVID-19: A Scoping Review

2021 ◽  
Vol 18 (5) ◽  
pp. 2336
Author(s):  
Munazza Fatima ◽  
Kara J. O’Keefe ◽  
Wenjia Wei ◽  
Sana Arshad ◽  
Oliver Gruebner
Keyword(s):  
Disease Mapping ◽  
Geospatial Analysis ◽  
Hotspot Analysis ◽  
Spatial Studies ◽  
Epidemiological Modeling ◽  
Wide Range ◽  
Scoping Reviews ◽  
Disease Pattern ◽  
The Usa ◽  
Meta Analyses

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data—along with scarcity of fine-scaled demographic, environmental, and socio-economic data—which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

scholarly journals Examining the Neighborhood Attributes of Recently Housed Partner Violence Survivors in Rapid Rehousing

2021 ◽  
Vol 18 (8) ◽  
pp. 4177
Author(s):  
Charvonne N. Holliday ◽  
Kristin Bevilacqua ◽  
Karen Trister Grace ◽  
Langan Denhard ◽  
Arshdeep Kaur ◽  
...  
Keyword(s):  
Partner Violence ◽  
Housing Affordability ◽  
Structural Racism ◽  
Census Tracts ◽  
Social Familiarity ◽  
Choropleth Maps ◽  
Economic Tradeoffs ◽  
Safety And Stability ◽  
Depth Interviews ◽  
Neighborhood Level

Survivors’ considerations for re-housing following intimate partner violence (IPV) are understudied despite likely neighborhood-level influences on women’s safety. We assess housing priorities and predictors of re-housing location among recent IPV survivors (n = 54) in Rapid Re-housing (RRH) in the Baltimore-Washington Metropolitan Area. Choropleth maps depict residential location relative to census tract characteristics (neighborhood deprivation index (NDI) and residential segregation) derived from American Community Survey data (2013–2017). Linear regression measured associations between women’s individual, economic, and social factors and NDI and segregation. In-depth interviews (n = 16) contextualize quantitative findings. Overall, survivors re-housed in significantly more deprived and racially segregated census tracts within their respective regions. In adjusted models, trouble securing housing (B = 0.74, 95% CI: 0.13, 1.34), comfortability with proximity to loved ones (B = 0.75, 95% CI: 0.02, 1.48), and being unsure (vs unlikely) about IPV risk (B = −0.76, 95% CI: −1.39, −0.14) were significantly associated with NDI. Economic dependence on an abusive partner (B = −0.31, 95% CI: −0.56, −0.06) predicted re-housing in segregated census tracts; occasional stress about housing affordability (B = 0.39, 95% CI: 0.04, 0.75) predicted re-housing in less segregated census tracts. Qualitative results contextualize economic (affordability), safety, and social (familiarity) re-housing considerations and process impacts (inspection delays). Structural racism, including discriminatory housing practices, intersect with gender, exacerbating challenges among survivors of severe IPV. This mixed-methods study further highlights the significant economic tradeoffs for safety and stability, where the prioritization of safety may exacerbate economic devastation for IPV survivors. Findings will inform programmatic policies for RRH practices among survivors.

scholarly journals Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps

2021 ◽  
Vol 10 (4) ◽  
pp. 208
Author(s):  
Christoph Traun ◽  
Manuela Larissa Schreyer ◽  
Gudrun Wallentin
Keyword(s):  
Time Series ◽  
Empirical Research ◽  
Global Trends ◽  
First Order ◽  
Choropleth Maps ◽  
Temporal Generalization ◽  
Perceptual Limits ◽  
Local Outlier

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.

scholarly journals Disease Mapping of Leprosy in Maluku Province, 2019 using Hierarchical Bayes

2021 ◽  
Vol 1863 (1) ◽  
pp. 012012
Author(s):  
I F Mahdy ◽  
R Fitriani ◽  
W D Revildy
Keyword(s):  
Disease Mapping ◽  
Hierarchical Bayes

Collection, Analysis, and Reporting of Kentucky Traffic Incident Management Performance

2021 ◽  
pp. 036119812110010
Author(s):  
Xu Zhang ◽  
Reginald R. Souleyrette ◽  
Eric Green ◽  
Teng Wang ◽  
Mei Chen ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Management Strategies ◽  
Incident Management ◽  
Vehicle Crashes ◽  
Clearance Time ◽  
Emergency Responders ◽  
Traffic Incident ◽  
Choropleth Maps ◽  
Traffic Incident Management ◽  
Secondary Crashes

Traffic incidents remain all too common. They negatively affect the safety of the traveling public and emergency responders and cause significant traffic delays. Congestion associated with incidents can instigate secondary crashes, exacerbating safety risks and economic costs. Traffic incident management (TIM) provides an effective approach for managing highway incidents and reducing their occurrence and impacts. The paper discusses the establishment and methods of calculation for five TIM performance measures that are used by the Kentucky Transportation Cabinet (KYTC) to improve incident response. The measures are: roadway clearance time, incident clearance time, secondary crashes, first responder vehicle crashes, and commercial motor vehicle crashes. Ongoing tracking and analysis of these metrics aid the KYTC in its efforts to comprehensively evaluate its TIM program and make continuous improvements. As part of this effort, a fully interactive TIM dashboard was developed using the Microsoft Power BI platform. Dashboard users can apply various spatial and temporal filters to identify trends at the state, district, county, and agency level. The dashboard also supports dynamic visualizations such as time-series plots and choropleth maps. With the TIM dashboard in place, KYTC personnel, as well as staff at other transportation agencies, can identify the strengths and weaknesses of their incident management strategies and revise practices accordingly.

scholarly journals Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

2021 ◽  
Vol 10 (7) ◽  
pp. 432
Author(s):  
Nicolai Moos ◽  
Carsten Juergens ◽  
Andreas P. Redecker
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Methodological Approach ◽  
Spatial Relationship ◽  
Postal Code ◽  
Annual Income ◽  
Choropleth Maps ◽  
Clustering Approach ◽  
Relationship Of ◽  
Insight Into

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

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