Fear,crime, and space: The case of Viçosa,Brazil

Crime is both a factual and perceptual component of the urban landscape, seemingly both a societal pathology and the consequence of economic disparity between social groups. Crime has a spatial structure that can be revealed by mapping. Urban crime has a spatial multiplier effect that changes the values and perceptions of how people see urban space, and which jeopardizes the quality of life of a city's inhabitants. In this research we examine the question of whether the geography of actual criminal acts is echoed by peoples' perceptions of crime, what might be termed their “spaces of fear”. We ask how the fear of crime is associated with reported urban crime. Urban crime incidents have been increasing in Viçosa, Minas Gerais, Brazil. We assembled crime information about Viçosa from two sources: first, crime as reported to the police and second, crime as perceived by city residents and measured by surveys and interviews. Reported criminal acts reveal a clustered geography, focusing particularly on the Downtown area, where there is a concentration of urban wealth and potential victims are more numerous. Offenses against property were focused on Downtown, while offenses against the person were located mostly in peripheral areas. The widespread feeling of insecurity in the city's neighborhoods, reflecting the fear of becoming a victim of violence and crime, was common throughout the city. Results confirmed the conclusion of past studies showing that the fear of violence and crime are not directly related to increasing numbers of criminal reports. Sites with higher incidence of crimes are not places with higher levels of fear. Rather than being geographically explainable “spaces of fear”, the spatial distribution of the fear of violence and crime appears to be unrelated in Viçosa, and neither is clustered or dispersed in any measurable way.     

Screening of thermophilic microalgae and cyanobacteria from Tunisian geothermal sources

Six geothermal sources distributed in the north and south of Tunisia were examined monthly during the period from August 2009 to April 2010, in order to determine mats communities' abundance and biodiversity. Twenty-seven taxa, belonging to three taxonomic groups: Cyanobacteria, Bacillariophyta and Chlorophyta were identified. The Cyanobacteria group was more prominent with a particular dominance of two genera Phormidium and Spirulina. Their maximum abundance was respectively 919.2 ± 44.2 10³ cells ml⁻¹ and 652.8 ± 92.6 10³ cells ml⁻¹. The dominant mats morphologies in prospected sites were filaments. Results demonstrate that biodiversity values ranged between 0.16 and 1.15 bits. cell⁻¹. The spatial variations showed that the regional specificity in mats species biodiversity and abundance between north and south were not signified, whereas, each site had specific species. Temporal variations showed an increase of microalgae and prokaryotes abundance in the dry season.     

Gender bias in the convergence dynamics of the regional homicide rates in Mexico

The geography of violence in Mexico has changed in recent years because of an explosive increase in regional homicide rates since 2006. This study relies on spatial statistical data analysis to address the issue of convergence in homicide victim rates across Mexican municipalities from 2001 to 2010. Based on the results of spatial panel modeling, we conclude that despite strong regional disparities in murders, municipal-level homicide growth rates display a pattern of convergence with spatial interaction features. This convergent pattern is gender-specific; the homicide growth rates for females show stronger convergence than those for males when local and regional factors are not considered. We postulate that homicide growth dynamics among females more closely follow the predictions of the “modernization hypothesis” than do the dynamics among males. This finding suggests that violence (specifically, homicides) against women in Mexico is driven by underlying factors other than short-term factors, such as drug cartel dynamics, and regional factors, such as local institutions and governments. The data might support the long-established feminist hypothesis that contends that violence against women follows specific dynamics that are different from the dynamics of violence against men.     

珠三角空间梯度葫芦藓的重金属含量及其环境指示意义

以珠三角空间梯度上的核心区广州、近郊区肇庆鼎湖及远郊区怀集三地的葫芦藓为研究对象,对其重金属污染进行研究。结果显示:珠三角地区葫芦藓重金属含量呈明显的梯级分布,其中Cu、Pb、Zn、Cr、Cd含量均表现为广州最高,分别为47.64、68.83、252.87、64.20和1.02mg/kg;肇庆次之,分别为36.34、40.51、143.49、51.29和0.64mg/kg;怀集最低,分别为16.42、11.80、97.13、32.43和0.21mg/kg。珠三角地区葫芦藓内不同重金属的含量差异反映了来源差异,其中Cd、Pb、Zn和Cu之间呈显著正相关关系,反映了重金属元素来自于相同的污染源;而Cr与Cd、Pb、Cu和Zn的相关性相对弱一些,说明Cr的污染源与其他元素存在一些差异,或者说这个元素与其他元素同时污染大气的可能性相对小一些。分析进一步揭示,三地葫芦藓污染同三地大气污染以及工业化进程存在一定的耦合性。基于此,指出葫芦藓可作为研究环境污染的指示标志,具有重要的环境指示意义。     

大伦敦地区空间战略规划的评介与启示

自1944年阿伯克隆比主持编制了世界上第一部特大型城市区域规划-《大伦敦规划》以来,每一阶段的伦敦地区发展规划都主要立足于自身需求和可行的程序性工具,以利于指导首都地区未来20-30年的发展。进入21世纪,作为英国第一个引入空间发展战略的地区,伦敦先后三次制定出台了大伦敦地区规划。本文以不同社会经济发展背景下的伦敦规划演变为基础,重点对大伦敦地区空间发展战略规划(2011)的核心内容、重要特点及其相关启示进行评介和分析,以期促进我国特大型城市区域的规划创新。     

A Review of: Environmental Modelling with GIS.

Cartographic modelling operations provide powerful tools for analysing and manipulating geographic data in the raster data model. This research extends these operations to the vector data model. It first discusses how the spatial scopes of analysis can be defined for point, line, and polygon features analogous to the raster cell. Then it introduces the local, focal, and zonal operations available for vector features, followed by providing a prototype syntax that might guide the implementation of these operations. Through example applications, this research also demonstrates the usefulness of these operations by comparing them with traditional vector spatial analysis.     

Using GIS to analyse a severe forest blowdown in the Southern Rocky Mountains

Many studies have helped us understand where blowdowns occur, how forests regenerate and respond to large disturbances. However, few studies have analysed how physical and biological factors affect blowdowns or interact with one another to determine susceptibility to blowdown. These factors range from the natural physical setting to past disturbances, and human-related features, such as roads and timber harvest units. These factors were examined in this study. Patches of the blown-down forest were mapped for the 1997 Routt-Divide blowdown, which affected over 10 000 ha of subalpine forest in north-central Colorado. A systematic sampling scheme was used to extract information on predictors for sampling points inside and outside the blowdown. Data on predictors were used in Classification and Regression Trees (CART) and logistic regression to model blowdown occurrence and severity. Two models were applied, and both were able to predict with an overall success rate of 75% or more. These two models produced similar results, showing that the Routt-Divide blowdown was most influenced by factors pertaining to the physical setting: distance to the Continental Divide, wind exposure, elevation, aspect, and cover type. Surprisingly, soil permeability and water-holding capacity and distance to natural edges (e.g. forest-meadow) were not correlated significantly with the blowdown pattern.     

On the use of dimensioned measures of error to evaluate the performance of spatial interpolators

Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous.     

Testing the effects of positional uncertainty on spatial decision‐making

There is now increasing agreement that the uncertainty associated with spatial information should be represented to users in a manner that is comprehensive and unambiguous. To assist with this task, researchers have developed a variety of methods to portray spatial uncertainty. While there has been some testing of the effectiveness of these displays, the possible effects of such representations on decision‐making have not yet been thoroughly investigated. Indeed, studies from the psychological literature indicate that people do not always make the same decisions when presented with the same information, and they can also be sensitive to the effects of presentation, task, and context. This paper examines how the use of four different methods to represent positional uncertainty can affect spatial decision‐making. The authors found that extremely significant differences in participants' responses were exhibited, depending on the manner in which positional uncertainty was displayed, although little difference was observed in the ability of the participants to comprehend the four display methods. In addition, strong preferences were recorded for certain representations over others.