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.

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 Sentinel-2 Application to the Surface Characterization of Small Water Bodies in Wetlands

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1487 ◽  
Author(s):  
Jesús Pena-Regueiro ◽  
Maria-Teresa Sebastiá-Frasquet ◽  
Javier Estornell ◽  
Jesús Antonio Aguilar-Maldonado
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Surface Characterization ◽  
Management Strategies ◽  
Water Bodies ◽  
Kappa Index ◽  
Small Water ◽  
Management Actions ◽  
Sentinel 2A ◽  
Small Water Bodies

Developing indicators to monitor environmental change in wetlands with the aid of Earth Observation Systems can help to obtain spatial data that is not feasible with in situ measures (e.g., flooding patterns). In this study, we aim to test Sentinel-2A/B images suitability for detecting small water bodies in wetlands characterized by high diversity of temporal and spatial flooding patterns using previously published indices. For this purpose, we used medium spatial resolution Sentinel-2A/B images of four representative coastal wetlands in the Valencia Region (East Spain, Mediterranean Sea), and on three different dates. To validate the results, 60 points (30 in water areas and 30 in land areas) were distributed randomly within a 20 m buffer around the border of each digitized water polygon for each date and wetland (600 in total). These polygons were mapped using as a base map orthophotos of high spatial resolution. In our study, the best performing index was the NDWI. Overall accuracy and Kappa index results were optimal for −0.30 threshold in all the studied wetlands and dates. The consistency in the results is key to provide a methodology to characterize water bodies in wetlands as generalizable as possible. Most studies developed in wetlands have focused on calculating global gain or loss of wetland area. However, inside of wetlands which hold protection figures, the main threat is not necessarily land use change, but rather water management strategies. Applying Sentinel-2A/B images to calculate the NDWI index and monitor flooded area changes will be key to analyse the consequence of these management actions.

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 Effect of agricultural landscape characteristics on the hydrobiota structure in small water bodies

Hydrobiologia ◽  
2016 ◽  
Vol 793 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Tomasz Joniak ◽  
Natalia Kuczyńska-Kippen ◽  
Maciej Gąbka
Keyword(s):  
Agricultural Landscape ◽  
Water Bodies ◽  
Landscape Characteristics ◽  
Small Water ◽  
Small Water Bodies

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 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 EXPLORATORY SPATIAL DATA ANALYSIS (ESDA) ON COVID-19 CASES IN MALAYSIA

2021 ◽  
Vol 83 (6) ◽  
pp. 83-94
Author(s):  
Syerrina Zakaria ◽  
Nur Edayu Zaini ◽  
Siti Madhihah Abdul Malik ◽  
Wan Saliha Wan Alwi
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Movement Control ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Healthcare Provision ◽  
Continuous Increase ◽  
Study Results ◽  
Control Order

The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers.

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 Inequalities in the spatial concentration of agricultural crops among the micro- and mesoregions of Minas Gerais, Brazil

2020 ◽  
Vol 50 (5) ◽  
Author(s):  
Erica Costa Rodrigues ◽  
Ricardo Tavares ◽  
Adriana Lúcia Meireles
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Minas Gerais ◽  
The State ◽  
Spatial Data Analysis ◽  
P Value ◽  
Agricultural Crops ◽  
Analysis Model ◽  
Modernization Process ◽  
Local Moran

ABSTRACT: The present research aimed to map and estimate the spatial autocorrelation of agricultural crops of coffee, corn, soybeans, sugarcane and beans in the state of Minas Gerais and analyzed in the period from 2011 to 2015. The planted area data were obtained of the Systematic Survey of Agricultural Production - IBGE. The Exploratory Spatial Data Analysis model was used to calculate spatial autocorrelation using the Global and Local Moran Index. Significant spatial self-correction was reported in all studied cultures (P-value <0.05). The regions with the highest concentration of planted area are located in the western portion of the state. The least significant planting regions were the municipalities located in the Jequitinhonha and Vale do Mucuri regions. The results pointed to a micro and mesoregional inequality in the distribution of agricultural activities in the mining territory that seems to reflect the incomplete agricultural modernization process that occurred in the state in the 70 s and 80 s.

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