scholarly journals Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Mena ◽  
Cesar Sepúlveda ◽  
Eduardo Fuentes ◽  
Yony Ormazábal ◽  
Iván Palomo
Keyword(s):  
Spatial Analysis ◽  
Cardiovascular Diseases ◽  
Geographical Information Systems ◽  
Spatial Regression ◽  
Glycated Haemoglobin ◽  
Statistical Weight ◽  
Cluster Detection ◽  
Geographical Information ◽  
Local Cluster ◽  
Populations At Risk

Cardiovascular diseases (CVDs) are the primary cause of death and disability in de world, and the detection of populations at risk as well as localization of vulnerable areas is essential for adequate epidemiological management. Techniques developed for spatial analysis, among them geographical information systems and spatial statistics, such as cluster detection and spatial correlation, are useful for the study of the distribution of the CVDs. These techniques, enabling recognition of events at different geographical levels of study (e.g., rural, deprived neighbourhoods, etc.), make it possible to relate CVDs to factors present in the immediate environment. The systemic literature presented here shows that this group of diseases is clustered with regard to incidence, mortality and hospitalization as well as obesity, smoking, increased glycated haemoglobin levels, hypertension physical activity and age. In addition, acquired variables such as income, residency (rural or urban) and education, contribute to CVD clustering. Both local cluster detection and spatial regression techniques give statistical weight to the findings providing valuable information that can influence response mechanisms in the health services by indicating locations in need of intervention and assignment of available resources.

Intelligence from Space

2011 ◽  
pp. 194-226
Author(s):  
Paul Hendriks
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Specific Class ◽  
Step Procedure ◽  
Decision Making Processes ◽  
Projected Position ◽  
Spatial Element

The spatial element, which is omnipresent in data and information relevant to organizations, is much underused in the decision-making processes within organizations. This applies also to decision-making within the domain of Competitive Intelligence. The chapter explores how the CI function may benefit from developing a spatial perspective on its domain and how building, exploring and using this perspective may be supported by a specific class of information systems designed to handle the spatial element in data: Geographical Information Systems (GIS). The chapter argues that the key element for linking GIS to CI involves the identification of situations in which spatial analysis may support organizational decision-making within the CI domain. It presents a three-step procedure for identifying how CI may recognize spatial decision problems that are useful to boost the operation of the CI function. The first step concerns identifying relevant spatial variables, for instance by analyzing economic, demographic or political trends as to their spatial implications. The second step involves using GIS for positioning the organization with respect to the identified variables (present and projected position). The third step amounts to drawing strategic conclusions from Step 2 by assessing how the competition in relationship with the own organization would be positioned along the identified spatial analysis lines.

scholarly journals Statistical and Spatial Analysis of Hurricane-induced Roadway Closures and Power Outages

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1098 ◽  
Author(s):  
Mahyar Ghorbanzadeh ◽  
Mohammadreza Koloushani ◽  
Mehmet Baran Ulak ◽  
Eren Erman Ozguven ◽  
Reza Arghandeh Jouneghani
Keyword(s):  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Density Ratio ◽  
Geographical Information ◽  
Second Step ◽  
Power Outages ◽  
The Us ◽  
Infrastructure System ◽  
Detailed Assessment ◽  
The City

Hurricanes lead to substantial infrastructure system damages, such as roadway closures and power outages, in the US annually, especially in states like Florida. As such, this paper aimed to assess the impacts of Hurricane Hermine (2016) and Hurricane Michael (2018) on the City of Tallahassee, the capital of Florida, via exploratory spatial and statistical analyses on power outages and roadway closures. First, a geographical information systems (GIS)-based spatial analysis was conducted to explore the power outages and roadway closure patterns in the city including kernel density estimation (KDE) and density ratio difference (DRD) methods. In order to provide a more detailed assessment on which population segments were more affected, a second step included a statistical analysis to identify the relationships between demographic- and socioeconomic-related variables and the magnitude of power outages and roadway closures caused by these hurricanes. The results indicate that the high-risk locations for roadway closures showed different patterns, whereas power outages seemed to have similar spatial patterns for the hurricanes. The findings of this study can provide useful insights and information for city officials to identify the most vulnerable regions which are under the risk of disruption. This can lead to better infrastructure plans and policies.

scholarly journals Aligning Partners in Pediatric Health: Using Geographical Information Systems to Plan Community Coalitions

2020 ◽  
Vol 11 ◽  
pp. 215013272094051
Author(s):  
Margaret B. Nguyen
Keyword(s):  
Primary Care ◽  
Information Systems ◽  
Spatial Analysis ◽  
Child Health ◽  
Geographical Information Systems ◽  
Pediatric Asthma ◽  
Geographical Information ◽  
Community Coalitions ◽  
Community Partners ◽  
Ed Visits

Introduction: Compared with adults, children have higher emergency department (ED) utilization for asthma exacerbation. While community coalitions have been shown to prevent ED visits for asthma, there is little guidance on where to best implement these efforts. Geographical information systems (GIS) technology can help in the selection and coordination of potential coalition partners. This report proposes a model to be used by clinicians and child health equity advocates to strategize high-impact community health interventions. The aims were to identify the clusters of ED utilization for pediatric asthma, evaluate sociodemographic features of the population within the clusters, and identify potential primary care and school community partners. Methods: This model uses ED visit data from 450 nonmilitary California hospitals in 2012. We obtained ZIP code–level counts and rates for patients younger than 18 years discharged with a diagnosis code of 493 for asthma conditions from the California Office of Statewide Health Planning and Development’s Open Portal. We applied GIS spatial analysis techniques to identify statistically significant cluster for pediatric asthma ED utilization. We then locate the candidate community partners within these clusters. Results: There were 181 720 ED visits for asthma for all age groups in 2012 with 70 127 visits for children younger than 18 years. The top 3 geographic clusters for ED utilization rates were located in Fresno, Inglewood, and Richmond City, respectively. Spatial analysis maps illustrate the schools located within 0.5– and 1-mile radii of primary care clinics and provide a visual and statistical description of the population within the clusters. Conclusion: This study demonstrates a model to help clinicians understand how GIS can aid in the selection and creation of coalition building. This is a potentially powerful tool in the addressing child health disparities.

scholarly journals Cartographic Analysis of the Geomorphological Hazards of the Eastern Part of Sheikh Saad Sub-District by Using GIS

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Hind Fadhil Ibrahim Al-Jubouri ◽  
Firas S Raheem ◽  
Prof Dr Osama K Abdulridah ◽  
Prof Dr Ali A Kazem
Keyword(s):  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
High Efficiency ◽  
Geographical Area ◽  
Three Dimensional ◽  
Geographical Information ◽  
3D Analysis ◽  
Automated Methods ◽  
Modern Technologies

Geographical information systems are the latest applied computer technologies that contribute to supporting contemporary geographical studies through the possibility of working on preparing a database of geographical phenomena and modeling them in a digital form by providing automated methods and a set of systems and programs for managing and processing data with spatial and non-spatial reference, which is one of the important functions in geographic information systems And the base on which it depends to reach the optimal decisions to reveal the spatial relationships and correlations between geographical phenomena and with high efficiency, to become the contemporary method in the method of processing and spatial analysis of geographical information instead of the old traditional methods of geographical analysis, and the system also allowed the geographical area to enter into the era of modern technologies to evaluate phenomena. Geographical forecasting. The research materials and methods are determined by adopting topographical and geological maps, land-sat satellite visuals, and DEM data to form the search database, and based on the GIS program (Arc Map 9.3) and the (Global Mepper 11) program and the extensions of the (Arc Map 9.3) program, which are (Spatial Analysis) And the three-dimensional analysis (3D analysis), and the outputs are the final process through which the results of the research emerge. These outputs show the type of information that will be processed and presented in the form of three-dimensional maps and shapes, studying the most important causes of geomorphological risks for the study area, and developing solutions and treatments through the conclusions and recommendations of the research.

scholarly journals Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Kamukama Ismail ◽  
Gilbert Maiga ◽  
Denis Ssebuggwawo ◽  
Peter Nabende ◽  
Ali Mansourian
Keyword(s):  
Geographical Information Systems ◽  
Local Population ◽  
Temporal Trends ◽  
Distribution Patterns ◽  
Geographical Information ◽  
Global Public Health ◽  
Spatial Scan Statistic ◽  
Public Health Burden ◽  
Targeted Interventions ◽  
Populations At Risk

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

scholarly journals Diagnostic approaches to malaria in Zambia, 2009-2014

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Victor M. Mukonka ◽  
Emmanuel Chanda ◽  
Mulakwa Kamuliwo ◽  
Maha A. Elbadry ◽  
Pauline K. Wamulume ◽  
...  
Keyword(s):  
Risk Factors ◽  
Geographical Information Systems ◽  
Temporal Dynamics ◽  
Health Workers ◽  
Clinical Symptoms ◽  
Spatial Regression ◽  
Geographical Information ◽  
Diagnostic Tools ◽  
Control Center ◽  
Spatio Temporal

Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide.

scholarly journals Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

2020 ◽  
Vol 9 (2) ◽  
pp. 76 ◽  
Author(s):  
Naimat Ullah Khan ◽  
Wanggen Wan ◽  
Shui Yu
Keyword(s):  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Educational Institutions ◽  
Residential Areas ◽  
Temporal Modeling ◽  
Time Frequency ◽  
Usage Patterns ◽  
Spatio Temporal

The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

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