Advanced Spatial Analysis

2011 ◽  
pp. 174-202 ◽  
Author(s):  
Andrew Curtis ◽  
Michael Leitner
Keyword(s):  
Spatial Analysis ◽  
Nearest Neighbor ◽  
Interpolation Method ◽  
Spatial Regression ◽  
Spatial Association ◽  
Analytical Techniques ◽  
Spatial Filter ◽  
East Baton Rouge Parish ◽  
Spatial Regression Analysis ◽  
Cluster Methods

The last chapter presented several ideas of how to perform relatively simple forms of spatial analysis. Many of these approaches, though insightful, have been superceded by more advanced analytical techniques. This chapter will present a few of these approaches, namely methods of clustering, interpolation, and spatial association. Other concepts will also be addressed, such as spatial autocorrelation and the measures that can be used to find spatial clusters of significantly high (hot spots) or low (cold spots) values. Two additional cluster methods will also be discussed, these being nearest neighbor hierarchical clustering and the spatial filter. Kernel density interpolation will be introduced as the interpolation method for discrete incident locations. A discussion about spatial regression analysis will conclude this chapter. The analyses and examples shown in this chapter will again be based upon linked birth and death certificate data for East Baton Rouge Parish.

Quantum color image watermarking based on fast bit-plane scramble and dual embedded

2018 ◽  
Vol 16 (07) ◽  
pp. 1850060 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Peng Liu Yang ◽  
Xing Ao Liu ◽  
Hou Ian
Keyword(s):  
Nearest Neighbor ◽  
Color Image ◽  
Interpolation Method ◽  
Image Watermarking ◽  
Extraction Process ◽  
Watermark Image ◽  
Color Image Watermarking ◽  
Encryption Algorithms ◽  
Bit Plane ◽  
Watermarking Scheme

Most of the studied quantum encryption algorithms are based on square images. In this paper, based on the improved novel quantum representation of color digital images model (INCQI), a quantum color image watermarking scheme is proposed. First, INCQI improved from NCQI is used to represent the carrier and watermark images with the size [Formula: see text] and [Formula: see text], respectively. Secondly, before embedding, the watermarking needs to be preprocessed. That is, the watermark image with the size of [Formula: see text] with 24-qubits color information is disordered by the fast bit-plane scramble algorithm, and then further expanded to an image with the size [Formula: see text] with 6-qubits pixel information by the nearest-neighbor interpolation method. Finally, the dual embedded algorithm is executed and a key image with 3-qubits information is generated for retrieving the original watermark image. The extraction process of the watermark image is the inverse process of its embedding, including inverse embedding, inverse expand and inverse scrambling operations. To show that our method has a better performance in visual quality and histogram graph, a simulation of different carrier and watermark images are conducted on the classical computer’s MATLAB.

scholarly journals ANALISIS REGRESI SPASIAL DAN POLA PENYEBARAN PADA KASUS DEMAM BERDARAH DENGUE (DBD) DI PROVINSI JAWA TENGAH

2017 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
Inna Firindra Fatati ◽  
Hari Wijayanto ◽  
Agus M. Sholeh
Keyword(s):  
Spatial Regression ◽  
Spatial Models ◽  
Hemorrhagic Fever ◽  
Spatial Effect ◽  
Health Centers ◽  
Central Java ◽  
Local Moran ◽  
Spatial Regression Analysis ◽  
Different Characteristics ◽  
Central Java Province

Dengue Hemorrhagic Fever (DHF) is one of the diseases that threaten human health. The cases of dengue fever in the district / city certainly has different characteristics, geographic condition, the potential of the region, health facilities, as well as other matters that lie behind them. Based on local moran index values are visualized through thematic maps, some area adjacent quadrant tends to be in the same group. There are two significant quadrant in describing the pattern of spread of dengue cases namely quadrant high-high and lowlow. This indicates a spatial effect on the number of dengue cases, so that the spatial regression analysis. Based on the value of  and AIC, autoregressive spatial models (SAR) is good enough to be used in modeling the number of dengue cases in the province of Central Java. Factors that influence the number of dengue cases Central Java province in 2015 is the number of health centers per 1000 population, the number of polindes per 1000 population, population density (X3), percentage of people with access to drinking water sustainable decent (X6), the percentage of water quality net free of bacteria, fungi and chemicals (X7), and the number of facilities protected springs (X8).

scholarly journals Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

2021 ◽  
Author(s):  
Renato Quiliche ◽  
Rafael Renteria-Ramos ◽  
Irineu de Brito Junior ◽  
Ana Luna ◽  
Mario Chong
Keyword(s):  
Spatial Patterns ◽  
Research Question ◽  
Spatial Regression ◽  
Mortality Rates ◽  
Main Research ◽  
Goods And Services ◽  
Long Run ◽  
Response And Recovery ◽  
Pandemic Response ◽  
Spatial Regression Analysis

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

scholarly journals An Environmental GIS-based Variable Analysis of SARS-CoV-2 in the City of Recife, Brazil

2020 ◽  
Author(s):  
Arthur Souza ◽  
Caroline Mota ◽  
Amanda Rosa ◽  
Ciro Figueiredo ◽  
Ana Lucia Candeias
Keyword(s):  
Low Income ◽  
Public Services ◽  
Spatial Regression ◽  
Case Fatality ◽  
Global Scale ◽  
Environmental Data ◽  
Data Sets ◽  
Spatial Regression Analysis ◽  
Effective Drugs ◽  
The City

Abstract Background: Given the increasing rates at which cases of people infected by Covid-19 have been evolving to case-fatality rates on a global scale and the context of there being a world-wide socio-economic crisis, decision-making must be undertaken based on prioritizing effective measures to control and combat the disease since there is a lack of effective drugs and as yet no vaccine. Method: This paper explores the determinant factors of the COVID-19 pandemic and its impacts on Recife, Pernambuco-Brazil by performing both local and global spatial regression analysis on two types of environmental data-sets. Data were obtained from ten specific days between late April and early July 2020, comprehending the ascending, peak and descending behaviours of the curve of infections.Results: This study highlights the importance of identifying and mapping clusters of the most affected neighbourhoods and their determinant effects. We have identified that it is increasingly common for there to be a phase in which hotspots of confirmed cases appear in a well-developed and heavily densely-populated neighbourhood of the city of Recife. From there, the disease is carried to areas characterised by having a precarious provision of public services and a low-income population and this quickly creates hotspots of case-fatality rates. The results also help to understand the influence of the age, income, level of education of the population and, additionally, of the extent to which they can access public services, on the behaviour of the virus across neighbourhoods.Conclusion: This study supports government measures against the spread of Covid-19 in heterogeneous cities, evidencing social inequality as a driver for a high incidence of fatal cases of the disease. Understanding the variables which influence the local dynamics of the virus spread becomes vital for identifying the most vulnerable regions for which prevention actions need to be developed.

scholarly journals Analisis Regresi Spatial Durbin Model (SDM) untuk Pemodelan Kemiskinan Provinsi Jawa Timur Tahun 2017

2019 ◽  
Vol 12 (1) ◽  
pp. 40-46
Author(s):  
Nur Roudlotul Hidayah ◽  
Artanti Indrasetianingsih
Keyword(s):  
Regression Analysis ◽  
Spatial Regression ◽  
Statistical Technique ◽  
Predictor Variables ◽  
Poor People ◽  
Spatial Durbin Model ◽  
Classic Problem ◽  
Almost All ◽  
Spatial Regression Analysis ◽  
The Relationship

Regression is a statistical technique used to describe the relationship between response variables with one or more predictor variables. The development of classical regression analysis that is influenced by the effects of space or location of a region is called spatial regression analysis. The purpose of this study is to conduct Spatial Durbin Model (SDM) regression analysis for poverty modeling in East Java in 2017. Poverty is a classic problem that occurs in almost all countries and is multidimensional, which is related to social, economic, cultural and other aspects. In 2017, poverty in East Java declined compared to the previous year. Therefore it is necessary to identify the factors that influence poverty. The variables used are the percentage of poor people as the response variable (Y) and predictor variables including Education does not finish elementary school (X1), Literacy Rate age 15 -55 years (X2), informal sector workers (X3), unemployment rate open (X4), household users of land as the widest floor (X5), and households using improper sanitation (X6), and households using drinking water sources are not feasible (X7).    Regresi merupakan teknik statistik yang digunakan untuk menggambarkan hubungan antara variabel respon dengan satu atau lebih variabel prediktor. Pengembangan dari analisis regresi klasik yang dipengaruhi oleh efek ruang atau lokasi wilayah disebut analisis regresi spasial. Tujuan dari penelitian ini adalah untuk melakukan analisis regresi Spatial Durbin Model (SDM) untuk pemodelan kemiskinan di Jawa Timur tahun 2017. Kemiskinan merupakan masalah klasik yang terjadi hampir diseluruh negara dan bersifat multidimensional, dimana berkaitan dengan aspek sosial, ekonomi, budaya dan aspek lainnya. Pada tahun 2017, kemiskinan di Jawa Timur mengalami penurunan jika dibandingkan dengan tahun sebelumnya. Oleh karena itu perlu dilakukan identifikasi faktor-faktor yang berpengaruh terhadap kemiskinan. Variabel yang digunakan yaitu persentase penduduk miskin sebagai variabel respon (Y) dan variabel prediktor antara lain Pendidikan tidak tamat SD (X1), Angka Melek Huruf  (AHM) usia 15 -55 tahun (X2), pekerja sektor informal (X3), tingkat pengangguran terbuka (X4), rumah tangga pengguna tanah sebagai lantai terluas (X5), dan rumah tangga pengguna sanitasi tidak layak (X6), dan Rumah tangga pengguna sumber air minum tidak layak (X7).

scholarly journals Effects of Urban Density and City Size on Haze Pollution in China: Spatial Regression Analysis Based on 253 Prefecture-Level Cities PM2.5 Data

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaohong Liu
Keyword(s):  
Spatial Regression ◽  
Spillover Effect ◽  
Control Measures ◽  
City Size ◽  
Eastern Region ◽  
Concentration Data ◽  
Urban Density ◽  
Haze Pollution ◽  
Prevention And Control Measures ◽  
Spatial Regression Analysis

Based on the PM2.5 concentration data of 253 prefecture-level cities, it empirically investigated the effect of urban density on PM2.5 concentration in China using spatial econometric regression method. The results show that there is a spatial spillover effect of PM2.5 concentration in China. The coefficient of urban density is significantly negative, and the increase in urban density will reduce haze pollution. In order to reduce haze pollution in Chinese cities, it is necessary to continue to implement regional joint prevention and control measures. The eastern region should focus on building small blocks and high-density networks to reduce haze pollution. The central region should focus on urban greening, increase green coverage, and reduce the heat island effect. While improving the level of greening in the western region, we should increase urban density, reduce urban sprawl, and build compact cities.

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