scholarly journals Exploring spatial patterns of cardiovascular disease in Sweden between 2000 and 2010

2018 ◽  
Vol 46 (6) ◽  
pp. 647-658 ◽  
Author(s):  
Mohammadreza Rajabi ◽  
Ali Mansourian ◽  
Petter Pilesjö ◽  
Daniel Oudin Åström ◽  
Klas Cederin ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Moran’S I ◽  
Scan Statistic ◽  
Moran's I ◽  
Mortality And Morbidity ◽  
Admission Rates ◽  
Geographical Patterns ◽  
Causes Of Mortality

Aims: Cardiovascular disease (CVD) is one of the leading causes of mortality and morbidity worldwide, including in Sweden. The main aim of this study was to explore the temporal trends and spatial patterns of CVD in Sweden using spatial autocorrelation analyses. Methods: The CVD admission rates between 2000 and 2010 throughout Sweden were entered as the input disease data for the analytic processes performed for the Swedish capital, Stockholm, and also for the whole of Sweden. Age-adjusted admission rates were calculated using a direct standardisation approach for men and women, and temporal trends analysis were performed on the standardised rates. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I, together with Kulldorff’s scan statistic were applied to explore the geographical patterns of admission rates. Results: The rates followed a spatially clustered pattern in Sweden with differences occurring between sexes. Accordingly, hot spots were identified in northern Sweden, with higher intensity identified for men, together with clusters in central Sweden. Cold spots were identified in the adjacency of the three major Swedish cities of Stockholm, Gothenburg and Malmö. Conclusions: The findings of this study can serve as a basis for distribution of health-care resources, preventive measures and exploration of aetiological factors.

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

scholarly journals Roles and Clinical Applications of Exosomes in Cardiovascular Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Dong Guo ◽  
Yuerong Xu ◽  
Jian Ding ◽  
Jiaying Dong ◽  
Ning Jia ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Large Body ◽  
Bilayer Membrane ◽  
Therapeutic Agents ◽  
Therapeutic Strategies ◽  
Signal Molecules ◽  
Mortality And Morbidity ◽  
Causes Of Mortality ◽  
Bioactive Agents

Despite substantial improvements in therapeutic strategies, cardiovascular disease (CVD) is still among the leading causes of mortality and morbidity worldwide. Exosomes, extracellular vesicles with a lipid bilayer membrane of endosomal origin, have been the focus of a large body of research in CVD. Exosomes not only serve as carriers for signal molecules responsible for intercellular and interorgan communication underlying CVD pathophysiology but also are bioactive agents which are partly responsible for the therapeutic effect of stem cell therapy of CVD. We here review recent insights gained into the role of exosomes in apoptosis, hypertrophy, angiogenesis, fibrosis, and inflammation in CVD pathophysiology and progression and the application and mechanisms of exosomes as therapeutic agents for CVD.

scholarly journals Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Huling Li ◽  
Hui Li ◽  
Zhongxing Ding ◽  
Zhibin Hu ◽  
Feng Chen ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Cumulative Number ◽  
Moran's I ◽  
Spatial Statistical Analysis ◽  
Global Concern ◽  
And Cluster Analysis ◽  
Global Moran’S I

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

scholarly journals Significant spatial patterns from the GCM seasonal forecasts of global precipitation

2019 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Wei Zhang ◽  
Yongyong Zhang ◽  
Xiaohong Chen
Keyword(s):  
Spatial Patterns ◽  
Global Climate ◽  
Predictive Performance ◽  
Global Climate Models ◽  
Moran’S I ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Moran's I ◽  
Anomaly Correlation ◽  
Global Precipitation

Abstract. Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

scholarly journals A model-adjusted space–time scan statistic with an application to syndromic surveillance

2005 ◽  
Vol 133 (3) ◽  
pp. 409-419 ◽  
Author(s):  
K. P. KLEINMAN ◽  
A. M. ABRAMS ◽  
M. KULLDORFF ◽  
R. PLATT
Keyword(s):  
Temporal Trends ◽  
Space Time ◽  
Local History ◽  
Group Practice ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Geographical Patterns ◽  
Naturally Occurring ◽  
History Of ◽  
Time Scan

The space–time scan statistic is often used to identify incident disease clusters. We introduce a method to adjust for naturally occurring temporal trends or geographical patterns in illness. The space–time scan statistic was applied to reports of lower respiratory complaints in a large group practice. We compared its performance with unadjusted populations from: (1) the census, (2) group-practice membership counts, and on adjustments incorporating (3) day of week, month, and holidays; and (4) additionally, local history of illness. Using a nominal false detection rate of 5%, incident clusters during 1 year were identified on 26, 22, 4 and 2% of days for the four populations respectively. We show that it is important to account for naturally occurring temporal and geographic trends when using the space–time scan statistic for surveillance. The large number of days with clusters renders the census and membership approaches impractical for public health surveillance. The proposed adjustment allows practical surveillance.

scholarly journals Significant spatial patterns from the GCM seasonal forecasts of global precipitation

2020 ◽  
Vol 24 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Wei Zhang ◽  
Yongyong Zhang ◽  
Zhiyong Liu ◽  
Xiaohong Chen
Keyword(s):  
Spatial Patterns ◽  
Global Climate ◽  
Grid Cell ◽  
Predictive Performance ◽  
Moran’S I ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Moran's I ◽  
Anomaly Correlation ◽  
Global Precipitation

Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

scholarly journals Measuring The Impact of Spatial Perturbations on The Relationship Between Data Privacy and Validity of Descriptive Statistics

2020 ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods: We analyzed the impact of perturbation on spatial patterns in the full set of address- level mortality data from Lawrence, MA during the period from 1911-1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and the its compliance with HIPAA privacy standards. Results: Random perturbation at 50 meters, donut masking between 5 and 50 meters, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100x100 and 250x250 meter cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions: Using the set of published perturbation methods applied in this analysis, HIPAA- compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals ANALISIS POLA SPASIAL KEBAKARAN LAHAN DI KABUPATEN PULANG PISAU TAHUN 2001-2019

2020 ◽  
Vol 9 (1) ◽  
pp. 23
Author(s):  
Frederik Samuel Papilaya
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Spatial Correlations ◽  
Hotspot Analysis ◽  
Fire Event ◽  
Moran's I ◽  
Central Kalimantan ◽  
The Republic ◽  
The Relationship

Forest and land fires have become a problem for the Republic of Indonesia in the last few decades. Statistically based on MODIS data for 19 years from 2001 to 2019, Central Kalimantan is the province with the most number of fires totaling 255,334 fire spot or 18% of fire occurance from 34 provinces in Indonesia. This study will look at spatial patterns and spatial correlations of forest and land fires in Pulang Pisau District from 2001 to 2019. Spatial patterns of hotspots were analyzed using a statistical Getis-Ord (Gi *) analysis, the relationship between hotspots was analyzed using Spatial Autocorrelation Moran's I (Index). The results of the hotspot analysis show the high significance of the hotspots that occurred in the subdistricts of Sebangau Kuala, Kahayan Kuala, Kahayan Hilir and Jabiren. The result of spatial autocorrelation of hotspots from 2001-2019 (except in 2010) is that hotspots pattern are clustered. Based on the findings it can be concluded statistically that 99% of the likelihood of a group fire event occurred intentionally. This hotspot incident that arose intentionally can be a clue for the Local Government to be able to better engage the community to prevent and overcome the hotspot by providing coaching and trainingKeywords: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau Kebakaran hutan dan lahan telah menjadi masalah bagi Republik Indonesia dalam beberapa dekade terakhir. Secara statistik berdasarkan data MODIS selama 19 tahun mulai dari tahun 2001 sampai 2019, Kalimantan Tengah merupakan provinsi yang paling banyak memiliki titik api sejumlah 255.334 titik atau sebesar 18% kejadian kebakaran hutan dan lahan dari 34 provinsi di Indonesia. Penelitian ini akan melihat pola spasial dan korelasi spasial kebakaran hutan dan lahan di Kabupaten Pulang Pisau pada tahun 2001 sampai 2019. Pola spasial hotspot dianalisa dengan menggunakan analisis Getis-Ord (Gi*) statistic, hubungan antar titik api dianalisa dengan menggunakan Spatial Autocorrelation Moran’s I (Index). Hasil analisis hotspot menunjukan signifikasi tinggi hotspot yang terjadi pada kecamatan Sebangau Kuala, Kahayan Kuala, Kahayan Hilir dan Jabiren. Hasil spatial autocorrelation kejadian titik api dari tahun 2001-2019 (kecuali tahun 2010) adalah titik api memiliki pola yang merupakan berkelompok (clustered). Berdasarkan temuan bisa disimpulkan secara statistik bahwa 99% kemungkinan kejadian titik api yang berkelompok tersebut terjadi secara disengaja. Kejadian titik api yang muncul secara disengaja ini dapat menjadi petunjuk bagi Pemerintah Daerah untuk dapat lebih mengajak masyarakat dalam mencegah dan menanggulangi titik api dengan memberikan pembinaan dan pelatihan. Kata Kunci: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau

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