scholarly journals Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1789
Author(s):  
Mayra Rodriguez ◽  
Guangtao Fu ◽  
David Butler ◽  
Zhiguo Yuan ◽  
Keshab Sharma
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Green Infrastructure ◽  
Spatial Data Analysis ◽  
Urban Drainage ◽  
Spatial Clusters ◽  
Combined Sewer Overflows ◽  
Trade Offs ◽  
Underlying Mechanisms

This paper explores the spatial impact of green infrastructure (GI) location on the resilience of urban drainage systems by the application of exploratory spatial data analysis (ESDA). A framework that integrates resilience assessment, location sensitivity analysis and ESDA is presented and applied to an urban catchment in the United Kingdom. Three types of GI, namely a bioretention cell, permeable pavement, and green roof, are evaluated separately and simultaneously. Resilience is assessed using stress-strain tests, which measure the system performance based on the magnitude and duration of sewer flooding and combined sewer overflows. Based on the results of a location sensitivity analysis, ESDA is applied to determine if there is spatial autocorrelation, spatial clusters, and spatial outliers. Results show a stronger spatial dependency using sewer flooding indicators. Different GI measures present differences in spatial autocorrelation and spatial cluster results, highlighting the differences in their underlying mechanisms. The finding of conflicting spatial clusters indicates that there are trade-offs in the placement of GI in certain locations. The proposed framework can be used as a tool for GI spatial planning, helping in the development of a systematic approach for resilience-performance orientated GI design and planning.

scholarly journals Spatial Filtering with EViews and MATLAB

2016 ◽  
Vol 36 (1) ◽  
Author(s):  
Robert Ferstl
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Filtering ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Moran's I ◽  
Detection Of Outliers ◽  
Filtering Procedure

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran’s I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

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. 

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 Socio-Economic Assessment of Green Infrastructure for Climate Change Adaptation in the Context of Urban Drainage Planning

2020 ◽  
Vol 12 (9) ◽  
pp. 3792 ◽  
Author(s):  
Luca Locatelli ◽  
Maria Guerrero ◽  
Beniamino Russo ◽  
Eduardo Martínez-Gomariz ◽  
David Sunyer ◽  
...  
Keyword(s):  
Decision Making ◽  
Green Infrastructure ◽  
Waste Water Treatment ◽  
Economic Assessment ◽  
Environmental Benefits ◽  
Urban Drainage ◽  
Combined Sewer Overflows ◽  
Damage Reduction ◽  
Combined Sewer ◽  
Sewer Overflows

Green infrastructure (GI) contributes to improve urban drainage and also has other societal and environmental benefits that grey infrastructure usually does not have. Economic assessment for urban drainage planning and decision making often focuses on flood criteria. This study presents an economic assessment of GI based on a conventional cost-benefit analysis (CBA) that includes several benefits related to urban drainage (floods, combined sewer overflows and waste water treatment), environmental impacts (receiving water bodies) and additional societal and environmental benefits associated with GI (air quality improvements, aesthetic values, etc.). Benefits from flood damage reduction are monetized based on the widely used concept of Expected Annual Damage (EAD) that was calculated using a 1D/2D urban drainage model together with design storms and a damage model based on tailored flood depth–damage curves. Benefits from Combined Sewer Overflows (CSO) damage reduction were monetized using a 1D urban drainage model with continuous rainfall simulations and prices per cubic meter of spilled combined sewage water estimated from literature; other societal benefits were estimated using unit prices also estimated from literature. This economic assessment was applied to two different case studies: the Spanish cities of Barcelona and Badalona. The results are useful for decision making and also underline the relevancy of including not only flood damages in CBA of GI.

scholarly journals ESDA (Exploratory Spatial Data Analysis) of Vegetation Cover in Urban Areas—Recognition of Vulnerabilities for the Management of Resources in Urban Green Infrastructure

2020 ◽  
Vol 12 (5) ◽  
pp. 1933
Author(s):  
Ana Clara M. Moura ◽  
Bráulio M. Fonseca
Keyword(s):  
Data Analysis ◽  
Population Density ◽  
Spatial Data ◽  
Green Infrastructure ◽  
Vegetation Cover ◽  
Urban Areas ◽  
Spatial Behavior ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Local Index

From the mapping of urban vegetation cover by high-resolution orthoimages, using IR band and NDVI classification (Normalized Difference Vegetation Index), added to three-dimensional representation obtained by LiDAR capture (Light Detection and Ranging), the volumetric values of vegetal cover are obtained as a base to construct spatial analysis in the district of Pampulha, in Belo Horizonte, investigating the role it plays in the neighborhood. The article aims to analyze the relationship between vegetation cover, income distribution and population density, as a support to urban environmental quality management. It applies Exploratory Spatial Data Analysis (ESDA) to identify the presence of clusters and patterns of spatial distribution and to examine spatial autocorrelation. The results confirm the concentration of vegetation cover in areas of high income and lower population density but the main contribution of the study is the use of a method to analyze the spatial behavior of this distribution. Calculating Moran global index and local index (LISA), these spatial combinations are mainly used to identify transformation pressures, which may result in the definition of priorities for public actions and the construction of proposals for parameterization of vegetation cover to support plans related to green infrastructure in urban areas.

Spatial Pattern Evolution of Coal Consumption at Provincial Level in China

2013 ◽  
Vol 734-737 ◽  
pp. 1752-1756
Author(s):  
Kai Yong She ◽  
Wen Jun Chen
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Hot Spots ◽  
Spatial Interaction ◽  
Interaction Mechanism ◽  
Spatial Data Analysis ◽  
Management Policy ◽  
Coal Consumption ◽  
Cold Spots ◽  
Consumption Activity

Exploratory Spatial Data Analysis was used to analyze the evolvement of spatia1 pattern on coal consumption in China since 2002. General spatial autocorrelation of coal consumption in 31 provinces of China was analyzed by Morans I and Getis-Ord General G. Getis-0rd Gi* was used to test the local spatial dependence, identifying the spatial distribution of hot spots and cold spots. The results show that coal consumption per capita of 31 provinces in China exhibits an enhanced trend of spatial autocorrelation. The areas with similar level of coal consumption are clustered in space. The coal consumption activity can be affected by the neighborhoods and their own regions. Hotspot areas are mainly concentrated in North and Northeast China and continuously increase with time, coldspot areas are mainly concentrated in South China and constantly decrease by time. So government needs to consider the spatial interaction mechanism of coal consumption when establishing the energy management policy.

scholarly journals A Statistical Toolbox For Mining And Modeling Spatial Data

2017 ◽  
Vol 19 (5) ◽  
pp. 5-24
Author(s):  
Gérard D’Aubigny
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Point Of View ◽  
Spatial Data Analysis ◽  
Formal Framework ◽  
Mining Projects ◽  
Small Change ◽  
Spatial Entities

Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis.

scholarly journals Spatial analysis of plant species distribution among small water bodies in an agricultural landscape

2012 ◽  
Vol 61 (2) ◽  
pp. 93-101 ◽  
Author(s):  
Beata Bosiacka ◽  
Krzysztof Pacewicz ◽  
Paweł Pieńkowski
Keyword(s):  
Spatial Autocorrelation ◽  
Plant Species ◽  
Spatial Data ◽  
Agricultural Landscape ◽  
Water Bodies ◽  
Spatial Data Analysis ◽  
Small Water ◽  
Dispersal Potential ◽  
Limited Dispersal ◽  
Join Count Statistics

Due to their small size (0.02-1.0 ha), distinct boundaries, and conditions highly contrasting with those in the surrounding area, midfield water bodies are regarded as the so-called habitat islands. Their effective conservation calls for knowledge on their inhabitants' dispersal potential. However, direct empirical data are available for very few species only, but dispersal potential of a species may be inferred indirectly, from its distribution. The study addressed the question whether there is spatial autocorrelation in the distribution of plant species in midfield water bodies, or if the distribution is random. Spatial distribution of the midfield water bodies surveyed was analysed using the CrimeStat software, while spatial autocorrelation in distribution of 29 species was explored with the joincount.test routine of R CRAN software. Explorative spatial data analysis (ESDA) involving join-count statistics showed the presence of positive spatial autocorrelation in the distribution of ten hydro- and helophytic species. In their case, ESDA made it possible to reject the random distribution hypothesis, which opens up an avenue for exploring spatial patterns. Activities promoting the occurrence of species with limited dispersal potential should take into account their preferences in terms of shorter distances between neighbouring sites. This should make it possible to plan conservation of midfield water bodies not only as refuges, but also as stepping stone habitats facilitating migrations of wild species growing in an agricultural landscape.

scholarly journals Measuring Spatial Cluster for Leading Industries in Surakarta with Exploratory Spatial Data Analysis (ESDA)

2017 ◽  
Vol 18 (1) ◽  
pp. 64
Author(s):  
Muhammad Arif ◽  
Didit Purnomo
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Scale ◽  
Industrial Sector ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Spatial Approach ◽  
Leading Industry ◽  
The City

Economic clusters are significant to support the economic growth, particularly at regional scale. The approach in the analysis has evolved from the emphasis on the comparison between the intra and extra regional into the spatial approach that is capable to detect the prevailing movement and concentration pattern in particular economic activity, hence the generated data is more informative and analyzable. This paper concentrates in identifying the location and assessing the economic clusters of leading industries in Surakarta City, Indonesia based on the number of units and labor absorption by using the Exploratory Spatial Data Analysis (ESDA). In association with the first objective, ArcGis was employed to find out how the concentration of leading industries in Surakarta was formed. The analysis revealed that the industries in Surakarta City have a propensity to be remote from downtown and concentrated in the northern part of the city. The second objective was revealed by performing the Moran’s index on GeoDa software to determine the spatial autocorrelation among the observed areas as the basis in finding the leading industrial cluster. The analysis indicated that all leading industries have relatively low Moran’s index meaning there was no dominant leading industry in Surakarta. These results have been confirmed by the LISA method to reveal the areas having spatial autocorrelation for each industrial sector.

scholarly journals Spatial Analyses of Social Policies and of a Social Safety Net

2019 ◽  
Author(s):  
Vicky Albert ◽  
Jaewon Lim
Keyword(s):  
Growth Rates ◽  
Spatial Data ◽  
State Level ◽  
Safety Net ◽  
Spatial Data Analysis ◽  
Spatial Clusters ◽  
Southern States ◽  
Cash Assistance ◽  
Families With Children ◽  
Temporary Assistance

During the 2008 Great Recession, many families with children relied on cash assistance from Temporary Assistance for Needy Families (TANF) program. The present study applied Exploratory Spatial Data Analysis (ESDA) tools to analyze geographically varying spatial clusters of states’ unemployment rates, TANF caseload growth rates, TANF policy choices such as benefit levels and TANF responsiveness rates to the recession. We analyzed 45 contiguous states and Washington D.C. A standardized TANF responsiveness index was developed to compare states’ TANF growth rates relative to their labor market conditions. The western states were found to be very responsive to the recession with ratios greater than one. In contrast, Texas and Arizona, with ratios below 1, were unresponsive to the recession. The presence of strong spatial clusters in unemployment rate and TANF maximum aid were found. In the case of maximum aid, there was a strong presence of Low-Low spatial clusters in Southern States and High-High clusters in Northeastern States. The findings suggest that several neighboring states in the northeast and some in the south had similar levels of financial commitment during the 2008 recessionary as the ones found by earlier research conducted during non-recessionary periods. The findings have implications for future federal actions and for state level collaboration.

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