scholarly journals LO05: A statistical analysis to estimate the spatial dynamics of opioid-related emergency medical services responses in the city of Calgary 2017

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S8-S8
Author(s):  
M. Zhang ◽  
M. Mahsin ◽  
L. Huang ◽  
K. Fournier ◽  
Z. Li ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Spatial Autocorrelation ◽  
Emergency Medical Services ◽  
Random Effects ◽  
Poisson Regression ◽  
Community Intervention ◽  
Intervention Strategies ◽  
Spatial Random Effects ◽  
Global Spatial Autocorrelation ◽  
The City

Introduction: Understanding the spatial distribution of opioid abuse at the local level may facilitate community intervention strategies. The purpose of this analysis was to apply spatial analytical methods to determine clustering of opioid-related emergency medical services (EMS) responses in the City of Calgary. Methods: Using opioid-related EMS responses in the City of Calgary between January 1st through October 31st, 2017, we estimated the dissemination area (DA) specific spatial randomness effects by incorporating the spatial autocorrelation using intrinsic Gaussian conditional autoregressive model and generalized linear mixed models (GLMM). Global spatial autocorrelation was evaluated by Morans I index. Both Getis-Ord Gi and the LISA function in Geoda were used to estimate the local spatial autocorrelation. Two models were applied: 1) Poisson regression with DA-specific non-spatial random effects; 2) Poisson regression with DA-specific G-side spatial random effects. A pseudolikelihood approach was used for model comparison. Two types of cluster analysis were used to identify the spatial clustering. Results: There were 1488 opioid-related EMS responses available for analysis. Of the responses, 74% of the individuals were males. The median age was 33 years ( IQR: 26-42 years) with 65% of individuals between 20 and 39 years, and 27% between 40 and 64 years. In 62% of EMS responses, poisoning/overdose was the chief complaint. The global Morans Index implied the presence of global spatial autocorrelation. Comparing the two models applied suggested that the spatial model provided a better fit for the adjusted opioid-related EMS response rate. Calgary Center and East were identified as hot spots by both types of cluster analysis. Conclusion: Spatial modeling has a better predictability to assess potential high risk areas and identify locations for community intervention strategies. The clusters identified in Calgarys Center and East may have implications for future response strategies.

scholarly journals Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work

2020 ◽  
Vol 13 (4) ◽  
pp. 901-924
Author(s):  
David Buil-Gil ◽  
Angelo Moretti ◽  
Natalie Shlomo ◽  
Juanjo Medina
Keyword(s):  
Spatial Autocorrelation ◽  
Random Effects ◽  
Simulation Study ◽  
Small Area ◽  
Police Work ◽  
Best Linear Unbiased Predictor ◽  
Spatial Random Effects ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Estimation Models

Abstract There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these variables allow for using spatial random effects to improve small area estimation models and estimates’ reliability. This article introduces the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from neighboring areas, to place-based policing. It assesses the SEBLUP under different scenarios of number of areas and levels of spatial autocorrelation and provides an application to confidence in policing in London. The SEBLUP should be applied for place-based policing strategies when the variable’s spatial autocorrelation is medium/high, and the number of areas is large. Confidence in policing is higher in Central and West London and lower in Eastern neighborhoods.

Accuracy Analysis of DEM Based on Coons Surface

2011 ◽  
Vol 65 ◽  
pp. 214-217
Author(s):  
Yao Ge Wang ◽  
Peng Yuan Wang
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Boundary Curve ◽  
Digital Elevation ◽  
Grid Points ◽  
Elevation Model ◽  
Global Spatial Autocorrelation ◽  
Core Problem ◽  
Dem Model ◽  
Better Than

Interpolation is the core problem of Digital Elevation Model (DEM). The Coons DEM model is better than bilinear interpolation and moving surface fitting. It is constructed by grid boundary curve, the curve interpolates by some adjoining grid points. Its spatial pattern of error is random in global area, there is no significant global spatial autocorrelation, but it is an increasing trend along with the terrain average gradient increases.There is significant local spatial autocorrelation, the spatial pattern of error converges strongly in local areas.

scholarly journals How Far Has China’s Urbanization Gone?

2018 ◽  
Vol 10 (8) ◽  
pp. 2953 ◽  
Author(s):  
Yiping Xiao ◽  
Yan Song ◽  
Xiaodong Wu
Keyword(s):  
Urban Development ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Association ◽  
Early Years ◽  
Spatial Data Analysis ◽  
Rapid Urbanization ◽  
Urban Rural ◽  
Global Spatial Autocorrelation ◽  
Over Time

China’s rapid urbanization has attracted wide international attention. However, it may not be sustainable. In order to assess it objectively and put forward recommendations for future development, this paper first develops a four-dimensional Urbanization Quality Index using weights calculated by the Deviation Maximization Method for a comprehensive assessment and then reveals the spatial association of China’s urbanization by Exploratory Spatial Data Analysis. The study leads to three major findings. First, the urbanization quality in China has gradually increased over time, but there have been significant differences between regions. Second, the four aspects of urbanization quality have shown the following trends: (i) the quality of urban development has steadily increased; (ii) the sustainability of urban development has shown a downward trend in recent years; (iii) the efficiency of urbanization improved before 2006 but then declined slightly due to capital, land use, and resource efficiency constraints; (IV) the urban–rural integration deteriorated in the early years but then improved over time. Third, although the urbanization quality has a significantly positive global spatial autocorrelation, the local spatial autocorrelation varies between eastern and western regions. Based on these findings, this paper concludes with policy recommendations for improving urbanization quality and its sustainability in China.

scholarly journals Application of Cluster Analysis in Research on the Spatial Dimension of Penalised Behaviour

2021 ◽  
Vol 94 ◽  
pp. 97-120
Author(s):  
Andrzej Porębski
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Spatial Dimension ◽  
Sociology Of Law ◽  
Large Dataset ◽  
Objective Criterion ◽  
Administrative Division ◽  
Administrative Units ◽  
The City ◽  
Crime Reports

This paper is focused on some of the possibilities of the use of cluster analysis (clustering) in criminology and the sociology of law. Cluster analysis makes it possible to divide even a large dataset into a specified number of subsets in such a way that the resulting subsets are as homogenous as possible, and at the same time differ from each other substantially. When analysing geographical data, e.g. describing the location of crimes, the result of cluster analysis is a division of a territory into a certain number of coherent areas based on an objective criterion. The division of the territory under study into smaller parts is more insightful when the clustering method is applied compared to an arbitrary division into official administrative units. The paper provides a detailed description of hierarchical cluster analysis methods and an example of using the Ward’s hierarchical method and the k-means combinational method to divide data on crime reports in the city of Baltimore between 2014 and 2019. The analysis demonstrates that the resulting division differs considerably from the administrative division of Baltimore, and that increasing the number of groups emerging as a result of cluster analysis leads to an increase of variance of variables describing the structure of crime in individual parts of the city. The divisions obtained using clustering are used to verify the hypothesis on differences in crime structure in different areas of Baltimore. The main aim of the paper is to encourage the use of modern methods of data analysis in social sciences and to present the usefulness of cluster analysis in criminology and the sociology of law research.

scholarly journals DEVELOPMENT OF THE CAPITAL SOCIO-GEOGRAPHICAL REGION SETTLEMENT SYSTEM AS A FACTOR FOR THE FORMATION OF THE REGION TOURIST MARKET

2019 ◽  
pp. 84-91
Author(s):  
Yurii Solohub ◽  
Sergey Uliganets ◽  
Olha Bezpala
Keyword(s):  
Cluster Analysis ◽  
Urban Population ◽  
Geographical Region ◽  
Settlement System ◽  
City Center ◽  
Urban Settlement ◽  
Indirect Influence ◽  
Concentration Potential ◽  
Zone Of Influence ◽  
The City

Main goal: To analyze the level of the urban settlement system development of the Capital Socio-Geographical region by means of a cluster analysis method and by selecting the optimal number of capacitive indicators. It is assumed that the most significant characteristics, may be the most important and have a determining function. Methodology: The use of special statistical and mathematical methods of research, in particular, the method of cluster analysis is the basis of the study. This method has gained wide popularity for the study of both the general socio-economic development of the administrative-territorial units of the state and the corresponding systems of settlement of different taxonomic ranks. Cluster analysis is a research tool for analyzing data to solve classification problems. Its purpose is to sort cases into groups or clusters in such a way that the degree of dependency is strong between members within one cluster and weak between members of different clusters. The process of clustering involves the selection of optimal indicators, which most fully and objectively reflect the situation of the manifestation of a phenomenon in the studied area.Results: It is established that the presence of agglomerated settlements around the agglomeration center, namely the city of Kyiv, significantly increase its concentration potential, which leads to an increase in the area of both direct and indirect influence of the city center. Thus, the zone of influence of the city of Kyiv is not limited to the boundaries of the administrative Kyiv region, but extends beyond it, involving the territories of Chernihiv, Zhytomyr, Cherkasy and, to a lesser extent, Vinnitsa and Poltava regions. Scientific novelty: The clusterization of administrative-territorial units of the Capital Socio-Geographical region is carried out. Clustering was based on the degree of manifestation in them of the main indicators of the development of regional urban settlement systems.It is revealed that the presence of agglomerated settlements around the agglomeration center, the city of Kiev, significantly increase its concentration potential, which leads to an increase in the area of both direct and indirect influence of the city center. Thus, the zone of influence of the city of Kyiv is no longer confined to the boundaries of the administrative Kyiv region, but extends beyond it, involving the territories of Chernihiv, Zhytomyr, Cherkasy and, to a lesser extent, Vinnytsia and Poltava regions. The degree of localization of the urban population of the district and the cluster analysis of its administrative-territorial units in accordance with the levels of development of their settlement systems were considered to present the situation regarding the concentration of urban population of the Capital Socio-Geographical region. Practical relevance: Publication materials can be used in the development of measures to optimize the settlement system of the Capital Socio-Geographical region and to adjust the administrative and territorial reform of the state.

Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings

2016 ◽  
Vol 7 (1) ◽  
pp. 65-77 ◽  
Author(s):  
António Manuel Rodrigues ◽  
José António Tenedório
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Autocorrelation ◽  
Count Data ◽  
Data Aggregation ◽  
Scientific Field ◽  
Social Phenomena ◽  
Resident Population ◽  
Geometrical Data ◽  
Proximity Measures ◽  
Global Spatial Autocorrelation

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are greatly relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influences inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas - administrative regions and hexagonal surface tessellation - on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.

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