Gambling Outlets Location in Urban Areas: A Case Study of Madrid

Cities are certainly a key factor in the location of gambling facilities. This paper aims to map the location of gambling outlets in urban areas and to examine potential links between neighborhoods socioeconomic and demographic characteristics and gambling supply, taking into account spatial dependencies of neighboring areas. This correlation is of interest because neighborhood characteristics may attract sellers, and because the presence of gambling sellers may cause changes in neighborhood demographics. Using detailed official data from the city of Madrid for the year 2017, three spatial econometric approaches are considered: spatial autoregressive (SAR) model, spatial error model (SEM) and spatial lag of X (explicative variables) model (SLX). Empirical analysis finds a strong correlation between neighborhoods characteristics and co-location of gambling outlets, highlighting a specific geographic patterning of distribution within more disadvantaged urban areas. This may have interesting implications for gambling stakeholders and for local governments when it comes to the introduction and/or increase of gambling availability.

PEMODELAN REGRESI SPASIAL DEPENDENSI PADA TINGKAT PARTISIASI ANGKATAN KERJA DI INDONESIA TAHUN 2020

This study discusses the regression modeling with spatial dependence to determine the factors affecting the labor force participation rate in Indonesia 2020. The spatial regression models used in this study are spatial Autoregressive Model (SAR) and Spatial Error Model (SEM), The finding concludes that the SAR model is better used in spatial modeling. At the same time, provincial minimum wage, the average length of school or educational level, and population are factors that affect the labor force participation rate in Indonesia 2020.

Analisis Spasial pada Model Perekonomian Sumatera Bagian Selatan

This study tries to use the Spatial concept by analyzing the observed spatial patterns and spatial autocorrelation, as well as evaluating the spatial modeling of each region in 60 districts/cities in five Southern Sumatra Provinces. This research used Geoda. Geoda will then provide a spatial description of the condition of the percentage of GRDP presented in the Moran I statistics, LISA and LISA Clusterd Map in 2015-2019. The results of this study are expected to show the spatial relationship of GRDP between 60 regencies/cities in five provinces in Sumbagsel and be able to indicate how the spatial relationship is in the clustered pattern of regions with the same characteristics. Furthermore, the LISA Cluster map is expected to describe the grouping of GRDP in 11 regions. The SAR model was chosen to analyze cases of spatial linkage. This study will further provide an economic analysis of how the percentage of the population and GRDP influence, In addition, this study will examine how the influence of the Development Index and poverty on GRDP. Therefore, this research will be one of the studies that has the latest updates because it uses two approaches; spatial approach and economic approach presented in the results of the discussion and discussion.

Examining the spatial and non-spatial linkages between suburban housing markets

Purpose The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level. Design/methodology/approach The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018. Findings The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered. Originality/value The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

Regional analysis of diabetic retinopathy and co-existing social and demographic factors in the overall population of Poland

IntroductionThe aim of our study was to analyze the regional differences in diabetic retinopathy (DR) prevalence and its co-existing social and demographic factors in the overall population of Poland in the year 2017.Material and methodsData from all levels of healthcare services at public and private institutions recorded in the National Health Fund database were evaluated. International Classification of Diseases codes were used to identify patients with type 1 and type 2 diabetes mellitus (DM) and with DR. Moran's I statistics and Spatial Autoregressive (SAR) model allowed to understand the distribution of DR prevalence, and its possible association with environmental and demographic exposures..ResultsIn total, 310,815 individuals with diabetic retinopathy (DR) were diagnosed in the year 2017 in Poland. Of them, 174,384 (56.11%) were women; 221,144 (71.15%) lived in urban areas; 40,231 (12.94%) and 270,584 (87.06%) had type 1 and type 2 DM, respectively. The analysis of the SAR model showed a higher level of average income and a higher number of ophthalmologic consultations per 10 000 adults in the particular county were the significant factors for the occurrence of DR in the counties.ConclusionsThe analyses of social, demographic, and systemic factors co-existing with DR revealed that the level of income and the access to the ophthalmologic and diabetic service are crucial in DR prevalence in Poland.

Simulation of SAR signal processing system in telescopic mapping

At the present stage of development of surveillance tools, the most popular are radar tools. A special place among such systems is occupied by radars with a synthesized antenna aperture, which make it possible to obtain high-resolution radar images with a relatively small size of the antenna system. The creation of such radar facilities has a number of features, which increases the cost of the development process and increases the time frame, and verification of work can be complicated by the inability to form the required conditions. To eliminate such difficulties, it is advisable to create adequate mathematical models of systems that would allow you to conduct all the necessary tests and work out algorithms, with minimal time spent. In this paper, a phenomenological model of the reflected signal in SAR is proposed, and a SAR model is developed that has a block structure and can be supplemented with modules to identify new dependencies.