scholarly journals Growth of Lethal Violence in Brazil 2000–2017: A Space-Temporal Analysis of Homicides

2021 ◽  
pp. 104398622110343
Author(s):  
Temidayo James Aransiola ◽  
Vania Ceccato ◽  
Marcelo Justus
Keyword(s):  
Geographical Information Systems ◽  
Growth Models ◽  
Temporal Analysis ◽  
Policy Implications ◽  
Geographical Information ◽  
Steady Increase ◽  
Homicide Rates ◽  
Lethal Violence ◽  
The North ◽  
Show Evidence

This study investigates the space-temporal growth of homicide rates in Brazil from 2000 to 2017 and identifies determinants of the country’s growth of homicide rates. Data from the Brazilian Information System on Mortality and Censuses are used to estimate growth models combined with spatial statistics and Geographical Information Systems (GIS). Findings show evidence of change in the geographical distribution of lethal violence over time, characterized by a steady increase in the North and Northeast regions and a reduction in growth in the South and Southeast regions of Brazil. Social disorganization factors namely deprivation, ethnic heterogeneity, and urbanization are significant positive determinants of the growth of homicide rates. The results show a reduction of the predictive strength of income inequality and an increase in that of unemployment from the year 2010 to 2017. The theoretical and policy implications of these results are discussed.

scholarly journals The New Distribution: Spatio-Temporal Analysis of Large Distribution Warehouse Premises in England and Wales

2021 ◽  
Vol 6 (3) ◽  
pp. 399-414
Author(s):  
Paul Greenhalgh ◽  
Helen M. King ◽  
Kevin Muldoon-Smith ◽  
Josephine Ellis
Keyword(s):  
Geographical Information Systems ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Property Market ◽  
England And Wales ◽  
The North ◽  
Highway Network ◽  
Spatio Temporal ◽  
The Uk ◽  
Large Distribution

This research addresses the deficit of empirical investigation of changes in industrial and warehouse property markets in the UK. It uses business rates (rating list) data for England and Wales to reveal changes in the quantum and distribution of premises over the last decade. Spatio-temporal analysis using geographical information systems identifies where new industrial and warehouse premises have been developed and examines spatial changes in the distribution of premises between the two sectors. The research focuses on the development of new large distribution warehouses (LDWs) to investigate whether there is a new pattern of warehouse premises located in close proximity to junctions on the national highway network. Findings confirm the emergence of a dynamic distribution warehouse property market where “super sheds” have been developed in areas with high levels of multi-modal connectivity. The comprehensive spatio-temporal analysis of all industrial and warehouse premises in England and Wales reconfigures the previously recognised Midlands “Golden Triangle” of distribution warehouses to a “Golden Pointer” and reveals the emergence of a rival “Northern Dumbbell” of distribution warehouse premises in the North of England. Further analysis using isochrones confirms that 85% of the population of Great Britain is situated within four hours average heavy goods vehicle drive time of these two concentrations of super sheds and over 60% of all LDWs floorspace is within 30 minutes’ drive of intermodal rail freight interchanges.

scholarly journals Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

2021 ◽  
Vol 13 (3) ◽  
pp. 512
Author(s):  
Jairo Alejandro Gómez ◽  
ChengHe Guan ◽  
Pratyush Tripathy ◽  
Juan Carlos Duque ◽  
Santiago Passos ◽  
...  
Keyword(s):  
Urban Growth ◽  
Geographical Information Systems ◽  
Growth Models ◽  
A Priori ◽  
Remote Sensing Data ◽  
Public Utility ◽  
Town Planning ◽  
Geographical Information ◽  
New Town ◽  
Spatiotemporal Uncertainty

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

scholarly journals Numerical Modeling for Gully Erosion in the North of Algeria based on Data of Remote Sensing and Geographic Information Systems

2017 ◽  
Vol 25 (1) ◽  
pp. 37-63
Author(s):  
mohammad abbas daoudi mohammad abbas daoudi
Keyword(s):  
Remote Sensing ◽  
Multivariate Analysis ◽  
Information Systems ◽  
Geographical Information Systems ◽  
Univariate Analysis ◽  
Probabilistic Approach ◽  
Geographical Information ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
The North

The problems of soil erosion are largely widespread in the countries of the Mediterranean basin. The process of gullying is a complex phenomenon with disastrous consequences. It particularly affects northern Algeria, decreasing the potentialities of the water tanks, reducing cultivable lands availability and degrading infrastructures. Therefore, this work studies the analysis and the prediction of gullying erosion by using a probabilistic approach based on multisource data. The objective of this search is to answer to the three following questions: i) which factors support the process of gullying ? ii) how does a process of gullying develop? iii) which are the zones favourable to gullying ? Works are undertaken on the catchment area of the Isser River. We focused the applications on the upstream part of the basin. In this research, we study a North-South transect which corresponds to three under-basins slopes. The choice of these tests areas answers to four criteria defined in our method: the representativeness, the homogeneity, the availability of former data and, finally, the accessibility. After the completion of the multisource data, modelling and multivariate analysis for the prediction of gullying. The combination factor-process by the univariate analysis allows on the one hand, to highlight the variables controlling the process of gullying, and on the other hand, to analyse the variables on a hierarchical basis and to know their degree of influence. The multivariate analysis, by the logistic regression model (LRM), enabled us to select the significant variables and to locate the most favourable zones for the process of gullying. The validation of the models is evaluated using the curves of lift spin. The results suggest that the factors highlighted by the model to be most influential on gullying erosion are: the lithology, the slope, the morphopedology, the rainfall erosivity and the land cover. The synthesis of this approach is illustrated in the form of charts of gullying erosion risk maps in four classes of probability. The assessment of the study shows the fundamental interest of this approach using geographical information systems and remote sensing, in particular for the watersheds of the southern Mediterranean, with the possibility of extending this methodology to other regions.

scholarly journals ANALYSIS OF LAND USE CHANGES AND THEIR INFLUENCE ON SOIL FEATURES. CASE STUDY: SECAȘ VILLAGE, TIMIȘ COUNTY (ROMANIA)

2019 ◽  
Vol 13 (2) ◽  
pp. 157-166
Author(s):  
Loredana Copăcean ◽  
Ionut Zisu ◽  
Valentina Mazăre ◽  
Luminiţa Cojocariu
Keyword(s):  
Land Use ◽  
Rural Communities ◽  
Geographical Information Systems ◽  
Agricultural Land ◽  
Scientific Information ◽  
Land Use Changes ◽  
Arable Land ◽  
Geographical Information ◽  
Soil Features ◽  
The North

The soil, regarded as a natural resource, but also as a determinant element of the living standards of rural communities, manly agricultural, may be influenced, directly and indirectly, by the modality of land organizing and use. Starting from this consideration, through this study, the spatial and temporal evolution of land use is being pursued, particularly that of forest areas and wooded grasslands. The goal is to notice the changes that have occurred over a 30-year period and the manner how these changes are reflected on the soil features. The researches presented in this paper have been taking place in the north-eastern hilly area of Timiş County, that area having entirely a rural character. For realizing this study satellite images, topographical and cadastral maps, from different time periods, national and international databases, data from specialty literature were used. To all these we should add direct observations in the field, topographic surveys and information collected from local authorities. The processing of cartographic materials and data and scientific information has been realized with Geographical Information Systems specific applications. The obtained result has been expressed in the form of thematic maps, in graphic form or as statistical analysis. At the level of the analyzed area, the obvious changes in the land use, registered over time, are caused by a number of factors, such as: the organization form, from communist to capitalist policies, leaving agricultural land as fallow ground, reduction in livestock, changing land use etc. All these changes have caused the extension of the wooded grasslands, reduction of arable land, installing inferior forest vegetation in qualitative and quantitative terms etc. As a result, the soil, one of the most important natural resources, is degraded qualitatively, underexploited, and on the other hand, its role as a direct and indirect food producer for local communities is significantly reduced.

Spatial Online Analytical Processing (SOLAP)

2011 ◽  
pp. 298-319 ◽  
Author(s):  
Yvan Bedard ◽  
Sonia Rivest ◽  
Marie-Josée Proulx
Keyword(s):  
Information Systems ◽  
Geographical Information Systems ◽  
Temporal Analysis ◽  
Point Of View ◽  
Geographical Information ◽  
Spatial Component ◽  
On Line ◽  
Street Address ◽  
Analytical Processing ◽  
Spatio Temporal

It is recognized that 80% of data have a spatial component (ex. street address, place name, geographic coordinates, map coordinates). Having the possibilities to display data on maps, to compare maps of different phenomena or epochs, and to combine maps with tables and statistical charts allows one to get more insights into spatial datasets. Furthermore, performing fast spatio-temporal analysis, interactively exploring the data by drilling on maps similarly to drilling on tables and charts, and easily synchronizing such operations among these views is nowadays required by more and more users. This can be done by combining Geographical Information Systems (GIS) with On-Line Analytical Processing (OLAP), paving the way to “SOLAP” (Spatial OLAP). The present chapter focuses on the spatial characteristics of SOLAP from a geomatics engineering point of view: concepts, architectures, tools and remaining challenges.

scholarly journals Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

2020 ◽  
Vol 9 (2) ◽  
pp. 76 ◽  
Author(s):  
Naimat Ullah Khan ◽  
Wanggen Wan ◽  
Shui Yu
Keyword(s):  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Educational Institutions ◽  
Residential Areas ◽  
Temporal Modeling ◽  
Time Frequency ◽  
Usage Patterns ◽  
Spatio Temporal

The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

scholarly journals Geo-epidemiological reporting and spatial clustering of the 10 most prevalent cancers in Iran

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Ebrahim Babaee ◽  
Gholamreza Roshandel ◽  
Meysam Olfatifar ◽  
Arash Tehrani-Banihashemi ◽  
Arezou Ashaari ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Geographical Information Systems ◽  
Spatial Clustering ◽  
Incidence Rates ◽  
Geographical Information ◽  
Breast Cancers ◽  
Contributing Factors ◽  
Index Method ◽  
The North ◽  
Global And Local

Cancer is a problem of both global and local concern. We determined the geo-epidemiological and spatial distribution of the 10 most common cancers in Iran. We used the data of the Iranian Cancer Registry for the year 2014 analysing the prevalence of 112,131 registered cancer cases with the aim of detecting potential geographical underlying causes. The geographic distribution of cancers is reported as standardized incidence rates at the provincial level considering risk with respect to sex and age. A geographical information systems (GIS) approach based on Anselin Local Moran’s index method was used to map clusters and spatial autocorrelation patterns. The mean age of the patients was 55.6 (±17.8) and 61.7 (±18.2) for females and males, respectively, in the database which showed 46.1% (n=51,665) of all cases to be female. Analysis of the spatial distribution of cancers showed significant differences among the different provinces. Stomach and breast cancers were the most prevalent cancers in men and females, respectively. The highest incidence rates of stomach cancer were found in Ardabil and Zanjan provinces, with 48.38 and 48.08 per 100,000 population, respectively, while Tehran and Yazd provinces had the highest incidences of breast cancer, 51.0 and 47.5 per 100,000 population, respectively. Strong clustering patterns for stomach and breast cancers were identified in the north-western provinces and in Semnan Province, respectively. These patterns indicate a diversity of geo-epidemiological contributing factors to cancer incidence in Iran.

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