Land Subsidence Monitoring in Semarang, Indonesia through Optimized Hot Spot Analysis based on Time-Series InSAR Processing

2021 ◽  
Author(s):  
Wahyu Luqmanul Hakim ◽  
Seul Ki Lee ◽  
Chang-Wook Lee
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Hot Spot ◽  
Hot Spot Analysis ◽  
Subsidence Monitoring ◽  
Spot Analysis

Mapping Land Subsidence in Pekalongan, Indonesia using Time Series Interferometry and Optimized Hot Spot Analysis with Sentinel-1 SAR Data 

2021 ◽  
Author(s):  
Wahyu Luqmanul Hakim ◽  
Seul Ki Lee ◽  
Chang-Wook Lee
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Hot Spot ◽  
Northern Coast ◽  
Hot Spot Analysis ◽  
Persistent Scatterer ◽  
Radar Satellite ◽  
Deformation Patterns ◽  
Settlement Areas ◽  
Spot Analysis

<div> <p>Floods in Pekalongan, Indonesia often occur due to the overflowing of river water during heavy monsoon rain. While the northern coast area of Pekalongan which located adjacent to the Java sea was affected by coastal floods due to sea-level rise. The flood conditions in every area were exacerbated by land subsidence and lead to coastal inundation. Monitoring land subsidence in Pekalongan becomes important to predict the further possible land subsidence occurrence area and mitigate the possible hazard caused by land subsidence. The analysis of land subsidence is much easier since the existence of radar satellites. This study used Synthetic Aperture Radar (SAR) datasets from the Sentinel-1 radar satellite between 2017 and 2020 in descending tracks. The data was processed through a time-series Interferometry SAR (InSAR) method based on the Stanford Methods for Persistent Scatterer (StaMPS) algorithm to provide accurate measurements over large areas by improving the selection of coherent pixels and reducing atmosphere noises. The result of persistent scatterer points then spatially clustered using Optimized Hot Spot Analysis (OHSA) to estimate significant points statistically and define them as the hot spot points. The results of time-series vertical deformation in Pekalongan were compared with the GPS station measurements. The comparison showed a good correlation in deformation patterns between time-series InSAR and GPS measurements. Our study revealed that the land subsidence in Pekalongan occurred mostly in settlement areas under the young alluvium soil which did not support the maximum compression from many buildings. Another cause of land subsidence in Pekalongan was the excessive groundwater extraction in the settlement areas could reduce the effective stress of pore pressure and lead to compaction in the aquifer areas. The time-series method that using the StaMPS algorithm and Optimized Hot Spot Analysis in this study can be applied for monitoring land subsidence in another area and from all-terrain.</p> </div>

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

Early stage hot spot analysis through standard cell base random pattern generation

2017 ◽  
Author(s):  
Joong-Won Jeon ◽  
Jaewan Song ◽  
Jeong-Lim Kim ◽  
Seongyul Park ◽  
Seung-Hune Yang ◽  
...  
Keyword(s):  
Early Stage ◽  
Hot Spot ◽  
Pattern Generation ◽  
Random Pattern ◽  
Standard Cell ◽  
Hot Spot Analysis ◽  
Cell Base ◽  
Spot Analysis

scholarly journals Geographic variation in preventable hospitalisations across Canada: a cross-sectional study

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e037195
Author(s):  
Piotr Wilk ◽  
Shehzad Ali ◽  
Kelly K Anderson ◽  
Andrew F Clark ◽  
Martin Cooke ◽  
...  
Keyword(s):  
Geographic Variation ◽  
Small Area ◽  
Service Providers ◽  
Hot Spot ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Discharge Abstract Database ◽  
Cross Sectional ◽  
Hot Spot Analysis ◽  
Spot Analysis

ObjectiveThe objective of this study is to examine the magnitude and pattern of small-area geographic variation in rates of preventable hospitalisations for ambulatory care-sensitive conditions (ACSC) across Canada (excluding Québec).Design and settingA cross-sectional study conducted in Canada (excluding Québec) using data from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) linked prospectively to hospitalisation records from the Discharge Abstract Database (DAD) for the three fiscal years: 2006–2007, 2007–2008 and 2008–2009.Primary outcome measurePreventable hospitalisations (ACSC).ParticipantsThe 2006 CanCHEC represents a population of 22 562 120 individuals in Canada (excluding Québec). Of this number, 2 940 150 (13.03%) individuals were estimated to be hospitalised at least once during the 2006–2009 fiscal years.MethodsAge-standardised annualised ACSC hospitalisation rates per 100 000 population were computed for each of the 190 Census Divisions. To assess the magnitude of Census Division-level geographic variation in rates of preventable hospitalisations, the global Moran’s I statistic was computed. ‘Hot spot’ analysis was used to identify the pattern of geographic variation.ResultsOf all the hospitalisation events reported in Canada during the 2006–2009 fiscal years, 337 995 (7.10%) events were ACSC-related hospitalisations. The Moran’s I statistic (Moran’s I=0.355) suggests non-randomness in the spatial distribution of preventable hospitalisations. The findings from the ‘hot spot’ analysis indicate a cluster of Census Divisions located in predominantly rural and remote parts of Ontario, Manitoba and Saskatchewan and in eastern and northern parts of Nunavut with significantly higher than average rates of preventable hospitalisation.ConclusionThe knowledge generated on the small-area geographic variation in preventable hospitalisations can inform regional, provincial and national decision makers on planning, allocation of resources and monitoring performance of health service providers.

scholarly journals What are the contextual risk factors for low colorectal cancer screening uptake in El Paso County, Texas? Spatial cross-sectional analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e038342
Author(s):  
Jennifer Salinas ◽  
Jacquelyn Brito ◽  
Cheyenne Rincones ◽  
Navkiran K Shokar
Keyword(s):  
Colorectal Cancer ◽  
Census Tract ◽  
Hot Spot ◽  
El Paso ◽  
P Value ◽  
Hot Spot Analysis ◽  
Crc Screening ◽  
Screening Uptake ◽  
Fit Testing ◽  
Spot Analysis

ObjectiveThis study examines the geographical and socioeconomic factors associated with uptake of colorectal cancer (CRC) screening (colonoscopies or faecal immunochemical test (FIT) testing).DesignSecondary data analysis.SettingThe Against Colorectal Cancer in our Community (ACCION) programme was implemented in El Paso County, Texas, to increase screening rates among the uninsured and underinsured.ParticipantsWe successfully geocoded 5777 who were offered a free colonoscopy or FIT testing kit.Primary outcome measureCensus-tract CRC screening uptake average.ResultsMedicare recipient mortality (β=0.409, p-value=0.049) and % 65 years or older (β=−0.577, p value=0.000) were significant census tract contextual factors that were associated with the prevalence of CRC screening uptake in the geographically weighted Poisson regression. Neither Latino ethnicity nor immigrant concentration were significant predictors of CRC screening uptake in the ACCION programme. Hot spot analysis demonstrated that there was a significant low-value cluster in South Central El Paso. There was a similar hot spot for % 65 years or older in this same area, suggesting that uptake was lowest in an area that had the highest concentration of older adults.ConclusionThe results from this study revealed not only feasibility of hot spot analysis but also its utility in geographically tracking successful CRC screening uptake in cancer prevention and intervention programmes.

Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis

2019 ◽  
Vol 2673 (9) ◽  
pp. 684-695 ◽  
Author(s):  
Myungwoo Lee ◽  
Aemal J. Khattak
Keyword(s):  
Density Estimation ◽  
Motor Vehicle ◽  
Safety Concern ◽  
Hot Spot ◽  
Estimation Method ◽  
Spatial Relationship ◽  
Crash Severity ◽  
Hot Spot Analysis ◽  
Spot Analysis

Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.

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