Assessing Spatial Characteristics of Soil Lead Contamination in the Residential Neighborhoods Near the Exide Battery Smelter

2019 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
An-Min Wu ◽  
Jill Johnston
Keyword(s):  
Decision Making ◽  
Los Angeles ◽  
Spatial Data ◽  
Hot Spot ◽  
Lead Contamination ◽  
Spatial Data Analysis ◽  
Soil Lead ◽  
Industrial Activities ◽  
Lead Levels

The presence of hazardous chemicals such as lead (Pb) or other heavy metals in the environment poses significant threats to human health. Industrial activities can increase the concentrations of these toxic metals in the soil, water and air where people live, work and play. When exposed to lead, residents face a higher risk of neurological damage, anemia or developmental delays. Urban soil lead levels, for example, are usually higher than the natural background lead levels due to the historical usage of lead paint, leaded gasoline and proximity to industrial activities. We explored a case in southeastern Los Angeles County, where lead contamination in the soil has been a particular concern near a lead-acid battery smelter. In this case study, we investigated soil lead levels across the neighborhoods surrounding the smelter as a mean to support this clean-up decision making. We used a hot spot analysis to identify clusters of high soil lead levels at a neighborhood scale. This case study can be used to teach higher-division undergraduate and graduate students to incorporate spatial thinking and exploratory spatial analysis approaches into the decision-making process for remediation of environmental contamination. Through this case study, the students will develop the knowledge about soil lead contamination and associated health risks, learn how exploratory spatial data analysis can assist examining the distribution of soil lead contamination and discuss potential strategies to improve the environmental remediation process in the urban environment.

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

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. 

Assessing Spatial Equity: An Evaluation of Measures of Accessibility to Public Playgrounds

1998 ◽  
Vol 30 (4) ◽  
pp. 595-613 ◽  
Author(s):  
E Talen ◽  
L Anselin
Keyword(s):  
Spatial Data ◽  
Empirical Studies ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Socioeconomic Indicators ◽  
Model Average ◽  
Urban Service ◽  
Spatial Equity ◽  
Analytical Perspective

Geographical and political research on urban service delivery—who benefits and why—has proliferated during the past two decades. Overall, this literature is not characterized by a particular attention to the importance of method in drawing conclusions about spatial equity based on empirical studies. Specifically, there has been scant interest in the effect of geographic methodology on assessing the relationship between access and socioeconomic characteristics that are spatially defined. In this paper we take a spatial analytical perspective to evaluate the importance of methodology in assessing whether or not, or to what degree the distribution of urban public services is equitable. We approach this issue by means of an empirical case study of the spatial distribution of playgrounds in Tulsa, Oklahoma, relative to that of the targeted constituencies (children) and other socioeconomic indicators. In addition to the ‘traditional’ measure (count of facilities in an areal unit), we consider a potential measure (based on the gravity model), average travel distance, and distance to the nearest playground as indicators of accessibility. We find significant differences between the spatial patterns in these measures that are suggested by local indicators of spatial association and other techniques of exploratory spatial data analysis. The choice of access measure not only implies a particular treatment of spatial externalities but also affects conclusions about the existence of spatial mismatch and inequity.

Landslide Susceptibility Models Utilising Spatial Data Analysis Techniques. A Case Study from the Lower Deba Valley, Guipuzcoa (Spain)

2003 ◽  
Vol 30 (3) ◽  
pp. 267-279 ◽  
Author(s):  
Juan Remondo ◽  
Alberto González-Díez ◽  
José Ramón Díaz De Terán ◽  
Antonio Cendrero
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Spatial Data Analysis ◽  
Analysis Techniques

scholarly journals Effect of Exterior Home Renovation on Community Lead Hazards: A Pilot Study in South Bend, Indiana

2021 ◽  
Author(s):  
Kyle Moon ◽  
Meghanne Tighe ◽  
Joshua Brooks ◽  
J. Mike Coman ◽  
Heidi Beidinger-Burnett ◽  
...  
Keyword(s):  
Lead Level ◽  
Bare Soil ◽  
Soil Samples ◽  
Lead Contamination ◽  
Soil Lead ◽  
Lead Paint ◽  
The Us ◽  
Lead Levels ◽  
South Bend ◽  
Lead Hazards

Communities across the US face challenges from legacy lead contamination. In South Bend, Indiana, over 68,000 homes were built before 1978, and most contain leaded paint. When these homes are repainted, repaired, or renovated, failure to use lead-safe practices can contaminate the surrounding soil with lead paint flakes and dust. In this study, we used X-ray fluorescence (XRF) to measure soil lead levels surrounding a home with exterior leaded paint (about 10% Pb w/w) after it was repainted in fall of 2019. The painted wooden exterior was prepared for painting by dry scraping without the use of tarps or plastic barriers. A total of 220 soil samples were collected from the home and its immediate neighbors, and an additional 102 samples were collected from 34 homes in the same neighborhood. The median lead level in dripline soil samples across the neighborhood was 434 ppm, but in the recently repainted house, the median soil lead was 1808.9 ppm, and it was 1,346.4 ppm in the four neighboring homes. The repainted house and its four neighbors were mulched by covering all bare soil to a 4-6 inch depth with chipped wood mulch. Two months later, another 100 soil samples were collected and analyzed. The surface lead level around the target house dropped to 13.8 ppm, showing that mulching is an effective strategy for interim control of high soil lead levels.

scholarly journals Identifying Spatial Relationships between Built Heritage Resources and Short-Term Rentals before the Covid-19 Pandemic: Exploratory Perspectives on Sustainability Issues

2020 ◽  
Vol 12 (11) ◽  
pp. 4533 ◽  
Author(s):  
Irene Rubino ◽  
Cristina Coscia ◽  
Rocco Curto
Keyword(s):  
Spatial Data ◽  
Spatial Data Analysis ◽  
Spatial Relationships ◽  
Short Term ◽  
Historic Center ◽  
Built Heritage ◽  
Socio Economic Variables ◽  
City Relations

Built heritage resources (BHRs) are multidimensional assets that need to be conceived under a sustainability and circular economy framework. Whereas it is essential that their conservation, management, and enjoyment are sustainable, it is also necessary that the environmental, cultural, and socio-economic contexts in which they are integrated are sustainable too. Like other amenities, BHRs can improve the quality of the urban environment and generate externalities; additionally, they may influence sectors such as real estate, hospitality, and tourism. In this framework, this contribution aims to identify spatial relationships occurring between BHRs and short-term rentals (STRs), i.e., a recent economic phenomenon facilitated by platforms such as Airbnb. Through the application of Exploratory Spatial Data Analysis techniques and taking Turin (Italy) as a case study, this article provides evidence that spatial correlation patterns between BHRs and STRs exist, and that the areas most affected by STRs are the residential neighborhoods located in the proximity of the historic center of the city. Relations with other sets of socio-economic variables are highlighted too, and conclusions suggest that future studies are essential not only to monitor sustainability issues and reflect on new housing models and sustainable uses of buildings, but also to understand the evolution of the phenomenon in light of the pandemic Covid-19.

scholarly journals Multiple-Criteria Decision-Making for Assessing the Enhanced Geothermal Systems

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1597 ◽  
Author(s):  
Raos ◽  
Ilak ◽  
Rajšl ◽  
Bilić ◽  
Trullenque
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Risk Mitigation ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Decision Matrix ◽  
Technical Specifications

This paper presents the main features of a multiple-criteria decision-making tool for economic and environmental assessment of enhanced geothermal systems projects. The presented holistic approach takes into account important influencing factors such as technical specifications, geological characteristics, spatial data, energy and heat prices, and social and environmental impact. The multiple-criteria decision-making approach uses a weighted decision matrix for evaluating different enhanced geothermal systems alternatives based on a set of criterions which are defined and presented in this paper. The paper, defines and quantifies new criterions for assessing enhanced geothermal systems for a particular site. For evaluation of the relative importance of each criterion in decision making, the weight is associated with each of the listed criterions. The different scenarios of end-use applications are tested in the case study. Finally, in the case study, the data and statistics are collected from real geothermal plants. The case study provides results for several scenarios and the sensitivity analysis based on which the approach is validated. The proposed method is expected to be of great interest to investors and decision makers as it enables better risk mitigation.

scholarly journals Analysis on spatial distribution pattern of football fields: A case study in Wuhan, China

2021 ◽  
Vol 293 ◽  
pp. 02051
Author(s):  
Yuwei Wang ◽  
Kaicheng Wu ◽  
Wang Gao
Keyword(s):  
Spatial Data ◽  
Global Scale ◽  
Spatial Data Analysis ◽  
Wuhan City ◽  
The Sustainable Development ◽  
Aggregation Pattern ◽  
Per Capita ◽  
Football Field ◽  
Global And Local

In order to promote the sustainable development of sports facilities, the statistical data of football fields in 13 districts of Wuhan city were selected. Based on geographic information system (GIS) and exploratory spatial data analysis (ESDA) technology, this paper analyzed the global and local autocorrelation of the number of football fields per capita in Wuhan city. The results indicate that: (1) Comparing to the new districts in Wuhan, the number of football fields per capita in the central districts was relatively few. (2) In the global scale, the number of football fields per capita in Wuhan presented a spatial aggregation pattern. (3) In the local scale, the regional discrepancy and spatial clusters were confirmed in the local spatial autocorrelation analysis for football fields in Wuhan. At the same time, the research results could provide reasonable references for the planning of football field facilities.

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