Concentration of Urban Violence in Fortaleza and Strategies for Crime Prevention

2021 ◽  
pp. 073401682110380
Author(s):  
Régis Façanha Dantas ◽  
Serena Favarin
Keyword(s):  
Crime Prevention ◽  
Spatial Data ◽  
Spatial Regression ◽  
Spatial Dynamics ◽  
Spatial Data Analysis ◽  
Urban Violence ◽  
Violent Crimes ◽  
Spatially Correlated ◽  
The City ◽  
Urban Disorder

Despite the continued prevalence of violence in Latin America, there is a relative dearth of research investigating both spatial patterns of violent crimes and the effectiveness of evidence-based crime prevention policies in Brazil. This study aims to address this gap in extant knowledge by creating a Spatial Violence Index and a Restrictive Ambient Index to investigate the spatial dynamics of violent crimes and urban vulnerabilities in Fortaleza. Both exploratory spatial data analysis and spatial regression models were employed to visualize the associative patterns and measure the correlation between the two indexes. The results demonstrate how locations characterized by high levels of violence are spatially correlated with more vulnerable locations in terms of both socio-economic-demographics and urban disorder. Overall, the study identified 124 vulnerable micro-territories that would benefit from the allocation of resources in an effort to reduce violence in the city by enhancing the efficiency of policing and prevention strategies.

scholarly journals Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

scholarly journals SPATIAL PATTERN OF SUSTAINABLE URBAN DEVELOPMENT INDICATOR FOR THE MONTREAL URBAN COMMUNITY

2015 ◽  
Vol 39 (4) ◽  
pp. 220-231 ◽  
Author(s):  
Shohel Reza Amin ◽  
Umma Tamima
Keyword(s):  
Sustainable Development ◽  
Urban Development ◽  
Spatial Pattern ◽  
Spatial Data ◽  
Urban Community ◽  
Spatial Data Analysis ◽  
Sustainable Urban Development ◽  
Development Indicator ◽  
Development Plans ◽  
The City

The City of Montreal initiated a First Strategic Plan for Sustainable Development in 2005 followed by a Community and Corporate Sustainable Development Plan in 2010–2015. This study proposes a sustainable urban development indicator (SUDI) for each Montreal Urban Community (MUC) to evaluate the achievements of sustainable development plans. This study identifies thirty-two variables as the attributes of sustainable urban development. The multivariate technique and Exploratory Spatial Data Analysis are applied to determine the spatial pattern of SUDI for each MUC. The spatial pattern of SUDI identifies that Ville Marie, Verdun, Sud-Ouest, Mercier-Hochelaga-Maisonneuve and Plateau Mont-Royal have strong sustainable development. The findings of this study help the City of Montreal to understand the improvement of the sustainable development plans for Montreal city and to distribute the municipal budget for the community benefits accordingly.

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 Travel and us: The impact of mode share on sentiment using geo-social media and GIS

2021 ◽  
Author(s):  
Greg Rybarczyk ◽  
Syagnik Banerjee ◽  
Melissa D. Starking-Szymanski ◽  
Richard Ross Shaker
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Spatial Regression ◽  
Negative Influence ◽  
Spatial Data Analysis ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Factors Affecting ◽  
Pedestrian Travel ◽  
The Impact

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

scholarly journals Travel and us: The impact of mode share on sentiment using geo-social media and GIS

2021 ◽  
Author(s):  
Greg Rybarczyk ◽  
Syagnik Banerjee ◽  
Melissa D. Starking-Szymanski ◽  
Richard Ross Shaker
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Spatial Regression ◽  
Negative Influence ◽  
Spatial Data Analysis ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Factors Affecting ◽  
Pedestrian Travel ◽  
The Impact

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

We Know Where You Are

2016 ◽  
Vol 7 (2) ◽  
pp. 61-75 ◽  
Author(s):  
Xining Yang ◽  
Xinyue Ye ◽  
Daniel Z. Sui
Keyword(s):  
Social Media ◽  
Spatial Data ◽  
Space And Place ◽  
Spatial Data Analysis ◽  
Food Services ◽  
Point Of Interest ◽  
Geographic Context ◽  
Retail Shopping ◽  
The City ◽  
Media Data

The convergence of social media and GIS provides an opportunity to reconcile space-based GIS and place-based social media. For this purpose, the authors conduct an empirical study in Columbus, Ohio, aiming to enrich both the spatial and platial context of geo-tagged data, using location-based social media Foursquare checkins as an example. An exploratory analytical approached is used to enrich the geographic context of social media data in both space and place. Specifically, exploratory spatial data analysis and point of interest matching are applied to analyze about 50,000 checkins crawled from social media feeds. It is found that checkins tend to be spatially clustered near the center of the city. Popular places related to food, services, and retail shopping venues are more likely to be reported by social media users. The authors also conducted platial analysis of the top 25 popular place venues in the study area.

scholarly journals Spatial-Temporal Evolution Characteristics and Countermeasures of Urban Innovation Space Distribution: An Empirical Study Based on Data of Nanjing High-Tech Enterprises

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shuang Tang ◽  
Jingxiang Zhang ◽  
Fangqu Niu
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Temporal Evolution ◽  
Spatial Data Analysis ◽  
Space Distribution ◽  
High Tech ◽  
Innovation Activities ◽  
Evolution Characteristics ◽  
Innovation Space ◽  
The City

Since the financial crisis in 2008, innovation has gradually become the orientation of global economic development and the strategic choice for China’s urban development. With the transformation of the urban development mode from factor-driven and capital-driven to innovation-driven, many innovation spaces have begun to emerge in cities, which attract academic attention. A large number of studies on the relation between innovation activities and geographic space mainly focus on the phenomena at the regional level, and the city is only regarded as a target of innovation activities agglomeration. The study on the distribution of innovation space within the city is insufficient. In particular, there is a lack of studies on the spatial-temporal evolution of urban innovation space distribution. However, the study on the spatial-temporal evolution characteristics of urban innovation space distribution can provide planning countermeasures for the construction of innovative cities in China. Taking Nanjing as an empirical area, the spatial-temporal evolution of urban innovation space distribution was studied through methods such as average nearest neighbor, standard deviational ellipse, kernel density estimation, and exploratory spatial data analysis based on the data of high-tech enterprises identified from 2008 to 2019. The results showed the following: (1) the distribution of urban innovation space has significant spatial agglomeration characteristics, and the degree of agglomeration continued to rise; (2) regardless of the macro- or microperspective, the distribution of urban innovation space has shown the characteristic of diffusion at the initial, but the trend of polarization in recent years is significant; and (3) the distribution of urban innovation space exhibited diverse agglomeration modes and evolution trends in different regions, and it can be divided into three categories: grouped, banded, and scattered.

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