scholarly journals Spatial analysis of cerebral palsy in children and adolescents and its association with health vulnerability

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcus Valerius Peixoto ◽  
Andrezza Marques Duque ◽  
Allan Dantas Santos ◽  
Shirley Verônica Almeida Melo Lima ◽  
Társilla Pereira Gonçalves ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Data Analysis ◽  
Spatial Analysis ◽  
Children And Adolescents ◽  
Low Socioeconomic Status ◽  
Spatial Data ◽  
Local Level ◽  
Secondary Data ◽  
Spatial Data Analysis ◽  
Low Socioeconomic

Cerebral Palsy (CP) is commonly associated with low socioeconomic status. Use of spatial statistics and a Geographic Information Systems (GIS) are scarce and may contribute to the understanding of CP in a social context. To that end a spatial analysis of CP in children and adolescents was performed to analyze the association of CP with levels of vulnerability in a city (Aracaju, Sergipe) in north-eastern Brazil. In addition, an ecological study was conducted with data obtained from a populationbased survey and secondary data. Exploratory spatial data analysis and linear regression were used. A total of 288 CP cases were identified, with a prevalence of 1.65/1,000 and differences among city neighbourhoods ranging from 0-4/1,000. The mean age of cases studied was 9 years 1 month, with a standard deviation of 5 years 2 months. Most study subjects with cerebral palsy (163) were male (56.4%). The distribution of CP in the study population was not homogeneous throughout the territory. Some areas had clusters, with more cases associated with areas of high vulnerability. Spatial data analysis using GIS was useful to gain an epidemiological understanding of CP distribution that can guide decisionmaking with respect to production, distribution, and regulation of health goods as well as services at the local level.

scholarly journals KERENTANAN SOSIAL TERHADAP BANJIR DI BANTARAN SUNGAI BENGAWAN SOLO PASCA RELOKASI MANDIRI

2017 ◽  
Vol 19 (2) ◽  
pp. 105
Author(s):  
Agustina Setyaningrum ◽  
Dyah Rahmawati H ◽  
Muh. Aris Marfai
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Social Vulnerability ◽  
Random Sampling ◽  
Secondary Data ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Spatial Data Analysis ◽  
Primary Data ◽  
Multi Criteria Evaluation

<p class="judulabstrakindoCxSpFirst"><strong>ABSTRAK</strong></p><p class="abstrak">Banjir besar pada akhir tahun 2007 mengharuskan Pemerintah Kota Surakarta untuk melaksanakan program relokasi paska terjadinya banjir. Masyarakat pindah dan menempati lokasi relokasi namun tidak jauh dari bantaran Sungai Bengawan Solo. Penelitian ini bertujuan untuk menilai tingkat kerentanan sosial masyarakat terhadap banjir pasca relokasi yang bertempat tinggal di sempadan Sungai Bengawan Solo. Data yang digunakan dalam penelitian ini meliputi data primer dan data sekunder. Teknik pengambilan sampel yaitu <em>s</em><em>imple random sampling. </em>Analisis data keruangan dilakukan dengan metode <em>Spatial Multi Criteria Evaluation (SMCE)</em><em>. </em>Penilaian kerentanan dengan menggunakan dua skenario yaitu skenario lingkungan dan skenario ekonomi. Hasil proses SMCE menunjukkan bahwa di lokasi relokasi, terdapat wilayah-wilayah yang masuk dalam kerentanan sosial tinggi dan sedang. Berdasarkan skenario lingkungan, menunjukkan bahwa seluruh kelurahan/desa lokasi relokasi memiliki kerentanan tinggi kecuali Kelurahan Mojosongo yang memiliki kerentanan sedang. Berdasarkan skenario ekonomi, menunjukan lokasi relokasi yang termasuk dalam kerentanan tinggi adalah Kelurahan Semanggi, Jebres, dan Desa Gadingan. Sedangkan lokasi relokasi yang termasuk dalam kerentanan sedang dalam skenario ekonomi adalah Kelurahan Mojosongo, Desa Laban, dan Desa Plumbon.</p><p><strong>Kata k</strong><strong>unci</strong>: kerentanan, banjir, relokasi</p><p class="judulabstrakingCxSpMiddle"><strong>ABSTRACT</strong></p><p class="Abstrakeng">        Great flood at the end of 2007 requires Government of Surakarta to implement the relocation program after the flood. The community moved and occupied the relocation site but not far from the banks of Bengawan Solo River. The aims of the study are to assess the level of social vulnerability after relocation. The data used in this study consist of primary data and secondary data. The sampling technique used in this study was simple random sampling. Spatial data analysis was conducted using Spatial Multi Criteria Evaluation (SMCE). The vulnerability assessment using two scenarios, the environmental scenario and economic scenario. Results of the SMCE showed that in relocation sites there are areas that fall into high and medium social vulnerability. Based on the environmental scenarios, the relocation areas have high vulnerability except for Mojosongo which have moderate vulnerability. Based on the economic scenarios, the relocation area that included in high vulnerability are Semanggi, Jebres, and Gadingan.While the relocation area that included in moderate vulnerability using economic scenario are Mojosongo, Laban, and Plumbon.</p><p><strong><em>Keywords</em></strong><em>: </em><em>vulnerability, flood, relocation </em></p>

scholarly journals THE IMPACTS OF SOCIOECONOMIC FACTORS ON MIGRATION ACROSS TURKISH REGIONS: A SPATIAL DATA ANALYSIS

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Ilyas Oz ◽  
Fatih Celebioglu
Keyword(s):  
Data Analysis ◽  
Spatial Analysis ◽  
Unemployment Rate ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Security Problem ◽  
Spatial Dimensions ◽  
Quality Health ◽  
Choropleth Maps ◽  
High Level

When examining the causes of migration in Turkey, it can be seen that low quality health and education services, imbalanced urbanization, security problem, high level unemployment rate have pivotal role on migration. In the 1950s Turkey, with intensified migration to big cities (mostly to West part of the country), urbanization process has accelerated. This process brought a number of problems with itself.Although many studies have been performed by researchers about migration in Turkey, there is no paper which includes spatial analysis. In this manner, this study purpose to examine the impacts of the factors as unemployment rate, Socio-economic Development Index on migration and their spatial analysis dimensions.To test spatial dimensions of the variables, we perform an exploratory spatial data analysis (ESDA) on migration and other variables among provinces of Turkey. While our choropleth maps indicate that the some part of the country is significantly more developed than the others, the tools of spatial statistics reveal the presence of spatial dependence across provinces. The presence of heterogeneity is reflected in the distribution of LISA statistics. Overall, our results shed new light on the distribution of migration and its relation with the others among provinces across Turkey.

Analyzing The Violent Crime Patterns In Peninsular Malaysia : Exploratory Spatial Data Analysis (ESDA) Approach

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Syerrina Zakaria ◽  
Nuzlinda Abd. Rahman
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Violent Crime ◽  
Spatial Data ◽  
Hot Spot ◽  
Peninsular Malaysia ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Policy Makers ◽  
Violent Crime Rate

The objective of this study is to analyze the spatial cluster of crime cases in Peninsular Malaysia by using the exploratory spatial data analysis (ESDA). In order to identify and measure the spatial autocorrelation (cluster), Moran’s I index were measured. Based on the cluster analyses, the hot spot of the violent crime occurrence was mapped. Maps were constructed by overlaying hot spot of violent crime rate for the year 2001, 2005 and 2009. As a result, the hypothesis of spatial randomness was rejected indicating cluster effect existed in the study area. The findings reveal that crime was distributed nonrandomly, suggestive of positive spatial autocorrelation. The findings of this study can be used by the goverment, policy makers or responsible agencies to take any related action in term of crime prevention, human resource allocation and law enforcemant in order to overcome this important issue in the future. 

Interactive Spatial Data Analysis.

Ecology ◽  
1996 ◽  
Vol 77 (5) ◽  
pp. 1642
Author(s):  
Michael W. Palmer ◽  
Trevor C. Bailey ◽  
Anthony C. Gatrell
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis
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