Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling

In Indonesia, nearly seven million children under five are stunted and throughout the world, Indonesia is the country with the fifth-highest stunting prevalence. South Sulawesi ranks fourth with a high stunting potential in Indonesia. Stunting is caused by multi-dimensional factors and not only due to malnutrition experienced by pregnant women and children under five. In more detail, several factors that cause stunting are the effects of poor care, the lack of household/family access to nutritious food, and the lack of access to clean water and sanitation. In addition to maternal characteristics and parenting, the problem of stunting is also influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) so the spatial analysis is important to do in overcoming this problem. In spatial data, often observations at a location (space) depend on observations at other locations that are nearby (neighboring). By using Geographically Weighted Regression (GWR) obtained variables that affect the prevalence of stunting in South Sulawesi Province, including the percentage of babies receiving vitamin A intake, the percentage of babies receiving exclusive breastfeeding, the percentage of babies receiving health care, the percentage of malnourished children under five, the percentage short toddlers, the percentage of infants receiving DPT-HB-Hib, Measles and BCG immunizations.  for the GWR model is 81.32% and based on variables that are significant to the prevalence of stunting in South Sulawesi Province, three clusters are formed

Eksplorasi efektifitas model spasial untuk menjelaskan hubungan antara penduduk dan infrastruktur terhadap kesejahteraan masyarakat Kota Manado

This study was conducted to determine a family welfare model that is influenced by the population and infrastructure in Manado using spatial regression and spatial weighted regression, to analyze the factors that influence it and to examine the effectiveness of the spatial regression method in analyzing the case. The analysis used is the Spatial Error Model followed by Geographical Weighted Regression. The results of the analysis show that the predictor variables that affect the response variable are population, number of schools and health facilities. The assessment criteria prefer the GWR model to explain family welfare because it has a smaller AIC value than using the SEM model. Keywords: welfare family, OLS, SEM, GWRPenelitian ini dilakukan untuk menentukan model kesejahteraan keluarga yang dipengaruhi oleh jumlah penduduk dan infrastruktur di kota Manado menggunakan regresi spasial dan regresi terboboti spasial, menganalisis faktor-faktor yang mempengaruhinya serta mengkaji efektifitas metode regresi spasial dalam menganalisis kasus tersebut. Analisis yang digunakan yaitu Spatial Error Model (SEM) dilanjutkan dengan Geographicaly Weighted Regression (GWR). Hasil analisis menunjukkan bahwa variabel prediktor yang mempengaruhi variabel respon adalah jumlah penduduk, jumlah sekolah dan sarana kesehatan. Kriteria penilaian lebih memilih Model GWR untuk menjelaskan kesejahteraan keluarga karena memiliki nilai AIC lebih kecil dibandingkan menggunakan model SEM. &nbsp

Modelação das taxas de mortalidade associadas a hábitos alimentares nos municípios portugueses -uma análise exploratória utilizando geographically weighted regression

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementUma das principais preocupações da investigação em saúde é o desenvolvimento e melhoria dos indicadores que permitem monitorizar a saúde e/ou os fatores de risco e conectá-los ao ambiente da população. É sempre difícil medir a informação da população relativa à saúde porque existem muitos fatores a ter em conta. A Organização Mundial de Saúde (OMS) refere as taxas de mortalidade como relevantes indicadores para caracterizar a saúde geral da população. O principal objetivo deste estudo é identificar os indicadores socioeconómicos (ex. literacia, desemprego, poder de compra, consultas por habitante, restaurantes por habitante, etc.) e variáveis espaciais (ex. distância aos cuidados de saúde) que poderão ser associados com as taxas de mortalidade relacionadas com os hábitos alimentares, e os municípios onde são mais determinantes para essas taxas, com a finalidade de melhor compreender o estado de saúde nos municípios de Portugal continental. A metodologia utilizada é composta por duas fases principais. Primeiro, o conjunto das variáveis socioeconómicas e espaciais são analisadas utilizando um processo iterativo que aplica o Ordinary Least Squares (OLS), para obter diferentes modelos de regressão linear alternativos, selecionando-se o melhor modelo possível tendo em consideração diversos testes estatísticos e medidas de diagnóstico. De forma a lidar com a não estacionaridade espacial e para investigar as relações locais, na segunda fase utiliza-se o modelo Geographically Weighted Regression (GWR) com as variáveis utilizadas no melhor modelo de OLS. O modelo GWR é também diagnosticado para a multicolinearidade local das variáveis explicativas. A estatística Global Moran’s I é utilizada para diagnosticar a possível existência de autocorrelação espacial dos resíduos em todos os modelos testados. A precisão dos parâmetros estimados pelo GWR é avaliada através dos erros padrão locais. As variáveis utilizadas no modelo GWR foram “Resposta hospitalar”, “Rácio entre supermercados e lojas de conveniência”, “Distância média a restaurantes fast food por habitante” e “Percentagem de população com educação de 2º ciclo”, as quais explicam entre 42% e 64% da variabilidade das taxas de mortalidade nos municípios. O modelo tem um maior poder explicativo em alguns dos municípios da região centro, destacando-se os municípios do distrito de Coimbra. A variável “Distância média a restaurantes fast food por habitante” tem os coeficientes positivos em todos os municípios, os quais são mais elevados no centro litoral e na Área Metropolitana do Porto. As restantes variáveis têm coeficientes negativos e maior poder explicativo no interior do país. Espera-se que esta análise exploratória possa contribuir para o conhecimento das conexões locais entre os padrões socioeconómicos da população e as taxas de mortalidade relacionadas com os hábitos alimentares.One of the main concerns of health research is the development and improvement of the indicators that allow monitoring the health and/or risk factors and connect them with the population environment. It is always difficult to measure health information from the population because there are many factors to consider. The World Health Organization (WHO) refers the indicators related to mortality rates as relevant for the characterization of the overall population health. The main goal of this study is to identify socioeconomic indicators (e.g. illiteracy, unemployment, purchasing power, medical appointments by habitant, restaurants by habitant, etc.) and spatial variables (e.g. distance to health facilities) that might be associated with mortality rates caused by diseases associated to eating habits, and the municipalities where they are more determinant to these rates, in order to better understand the health status in the municipalities of mainland Portugal. The methodological framework has two main stages. In the first one, a set of socioeconomic and spatial variables are analyzed using an iterative process that applies Ordinary Least Squares (OLS) to obtain different alternative linear regression models, choosing the best possible model according to multiple statistical tests and diagnostic measures. In order to deal with spatial nonstationary and to investigate local relationships, the second stage is based on a Geographically Weighted Regression (GWR) model with the variables that were included in the best OLS model. The GWR model is also diagnosed for local multicollinearity of the explanatory variables. The Global Moran's I statistic is used to diagnose the possible existence of spatial autocorrelation of the residuals in all tested models. The accuracy of the GWR parameter estimates is assessed through the local standard errors. The selected variables in the GWR model were “Hospital response”, “Grocery-to-convenience stores ratio”, “Average distance to fast food restaurants per habitant” and “2nd cycle education”, which explain between 42% and 64% of the mortality rates variability in the municipalities. The model has greater explanatory power in some municipalities of the center region, with more relevance in Coimbra district. The local coefficients of the variable “Average distance to fast food restaurants per habitant” are positive in all municipalities, and are higher in the coastal center and in the metropolitan area of Porto. The remaining variables have negative coefficients and higher explanatory power inland. We hope that this exploratory spatial data analysis may contribute to the knowledge of the local connections and patterns of socioeconomic characteristics of the population and mortality rates caused by diseases related to eating habits in Portugal

Skill requirements and labour polarisation: An association analysis based on Polish online job offers.

Abstract. This paper uses the methodological scheme of contingency tables to explore polarisation in the Polish labour market. We use a large database of online job offers published on selected Polish job portals in the period 2017-2019, whereas most of the studies on the polarisation hypothesis are based on employment data. The main advantage of our microdata is the use of information on the required skills of the vacancy. The contingency table allows us to generate clusters of vacancies whose attributes tend to appear jointly. The study reveals that office skills do not offer a particular advantage in an automated labour market, while information and computer technology skills and communication skills seem to have a shield effect in such an environment. In addition, a cluster of transversal skills (self-organisational, technical and interpersonal skills) constitutes an important requirement for most job offers. These skills should be widely developed within the educational system, at different levels. Resumen. El trabajo emplea el esquema metodológico de las tablas de contingencia para explorar la polarización en el mercado de trabajo polaco. Usamos una amplia base de datos de ofertas de trabajo online publicadas en destacados portales de empleo polacos en el periodo 2017-2019, a diferencia de la mayoría de los estudios sobre la hipótesis de polarización, que están basados en datos de empleo. La principal ventaja de nuestros microdatos es el uso de información sobre las competencias requeridas de la vacante. La tabla de contingencia nos permite generar clusters de vacantes cuyos atributos tienden a aparecer conjuntamente. El estudio revela que las competencias de oficina no ofrecen una ventaja particular en un mercado de trabajo automatizado, mientras que las competencias de tecnologías de la computación y la información parecen tener un efecto protector en dicho entorno. Además, observamos que un cluster de competencias transversales (competencias de auto-organización, técnicas e interpersonales) constituye un requisito importante para la mayoría de las ofertas de trabajo. Estas competencias deberían ser ampliamente desarrolladas en el sistema educativo, en sus diferentes niveles.Departamento de Economía, Métodos Cuantitativos e Historia Económica. Universidad Pablo de Olavide

Analizy i prognozy polskiego rynku pracy. Przekrój powiatowy

Rynek pracy jest konglomeratem wielu subrynków, a jednym z kluczowych kryteriów podziału jest kryterium przestrzenne. Problematyka niniejszej monografii obejmuje szerokie spektrum zagadnień związanych z ilościową analizą rynku pracy w Polsce w ujęciu powiatowym. Badania przeprowadzone z zastosowaniem zróżnicowanych metod ilościowych (m.in. eksploracyjna analiza danych przestrzennych, metody wielokryterialnej klasyfikacji obiektów, niestrukturalne metody prognozowania) pozwoliły na identyfikację homogenicznych pod względem profilu gospodarczego klastrów powiatów oraz krótkookresową prognozę zatrudnienia w wyodrębnionych klastrach. Poszczególne działy gospodarki charakteryzują się odmiennymi prawidłowościami rozwojowymi i odmienną wrażliwością na zmiany koniunkturalne. Perspektywy rozwoju nie są jednakowe dla wszystkich rodzajów działalności gospodarczej, co determinuje sytuację na rynku pracy

Analyzing Chlamydia and Gonorrhea Health Disparities from Health Information Systems: A Closer Examination Using Spatial Statistics and Geographical Information Systems

Indiana University-Purdue University Indianapolis (IUPUI)The emergence and development of electronic health records have contributed to an abundance of patient data that can greatly be used and analyzed to promote health outcomes and even eliminate health disparities. However, challenges exist in the data received with factors such as data inconsistencies, accuracy issues, and unstructured formatting being evident. Furthermore, the current electronic health records and clinical information systems that are present do not contain the social determinants of health that may enhance our understanding of the characteristics and mechanisms of disease risk and transmission as well as health disparities research. Linkage to external population health databases to incorporate these social determinants of health is often necessary. This study provides an opportunity to identify and analyze health disparities using geographical information systems on two important sexually transmitted diseases in chlamydia and gonorrhea using Marion County, Indiana as the geographical location of interest. Population health data from the Social Assets and Vulnerabilities Indicators community information system and electronic health record data from the Indiana Network for Patient Care will be merged to measure the distribution and variability of greatest chlamydia and gonorrhea risk and to determine where the greatest areas of health disparities exist. A series of both statistical and spatial statistical methods such as a longitudinal measurement of health disparity through the Gini index, a hot-spot and cluster analysis, and a geographically weighted regression will be conducted in this study. The outcome and broader impact of this research will contribute to enhanced surveillance and increased effective strategies in identifying the level of health disparities for sexually transmitted diseases in vulnerable localities and high-risk communities. Additionally, the findings from this study will lead to improved standardization and accuracy in data collection to facilitate subsequent studies involving multiple disparate data sources. Finally, this study will likely introduce ideas for potential social determinants of health to be incorporated into electronic health records and clinical information systems

Geographically Weighted Regression in the Analysis of Unemployment in Poland

The main aim of this paper is an application of Geographically Weighted Regression (which enables the identification of the variability of regression coefficients in the geographical space) in the analysis of unemployment in Poland 2015. The study is conducted using 2015 statistical data for 380 districts (LAU 1) in Poland. The research results show that the determinants of unemployment are diverse in the geographic space and do not have a significant impact on unemployment rates in all spatial units (LAU 1). The existence of clusters of districts, characterised by the influence of the variables and a similar strength of interactions, is confirmed. Geographically Weighted Regression (GWR) proved to be an extremely effective instrument of spatial data analysis. The model had a considerably better fit with empirical data than the global model, and it enabled the drawing of detailed conclusions concerning the local determinants of unemployment in Poland