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.     

Modeling of space–time infectious disease spread under conditions of uncertainty

A theoretical model for the spread of infectious diseases in a composite space–time domain is developed. The model has a general form that enables it to account for the basic mechanisms of disease distribution and to incorporate the considerable multisourced uncertainty (caused by physiographic features, disease variability, meteorological conditions, etc.). Starting from the general model formulation regarding the specification of transmission and recovery rates, as well as the population migration dynamics, several subsequent assumptions are introduced that simplify analytical tractability and practical implementation. In particular, linearization involving a deterministic functional representation for the average evolution of the fraction of susceptible individuals allows the formulation of an extended Kalman filter approach for estimation based on the time series observed at a finite set of locations. Different aspects of interest derived from the epidemic space–time model proposed, as well as the performance of the extended Kalman filter procedure, are illustrated through simulations.     

Transformation of relational space in targeted poverty alleviation from the perspective of actor network: A case study of Hebian Village,Mengla County,Yunnan Province北大核心CSCD

精准扶贫中政府主导、企业和社会积极响应,共同助力贫困户脱贫,引致乡村关系空间在脱贫前后发生转化。论文使用参与式观察、半结构访谈和问卷调查等方法,从行动者网络视角重新审视云南省勐腊县河边村的精准扶贫,将关系空间转化划分为阶段内关系空间转化和阶段间关系空间转化,拓展了行动者网络理论对关系空间转化的解释,发现了河边村关系空间的阶段特征与转化路径:(1)精准扶贫中河边村的关系空间存在阶段性特征,包括脱贫攻坚阶段的前关系空间(标准空间较大)和巩固脱贫成果阶段的后关系空间(协商空间较大)。(2)阶段内关系空间转化是标准空间向协商空间转化的过程。以脱贫攻坚阶段为例,“两不愁、三保障”这一明确政策目标下的标准行为构建了较大的标准空间,有利于确立强制通行点,促进阶段内关系空间转化。(3)阶段间关系空间转化是前一阶段的协商空间转化为后一阶段的标准空间的过程,脱贫攻坚阶段的前协商空间就转化成了巩固脱贫成果阶段的后标准空间。这一关系空间转化的过程可结合新标准行为的应用和空间稳定性测度进行验证。(4)关系空间转化促成了村小组和村民转变为后关系空间中的关键行动者,他们在参与改善建成环境的过程中被动员起来,通过成立合作社的形式和其他行动者结成紧密网络联盟,未来会在巩固拓展脱贫攻坚成果、全面推进乡村振兴的过程中发挥关键作用。论文所构建的关系空间转化框架对分析和促进乡村地区的发展兼具理论价值与现实意义。     

Geostatistical space–time mapping of house prices using Bayesian maximum entropy

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space–time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.     

A 'culturally unsafe' space? The Somali experience of Christchurch secondary schools

Abstract:  School should be a 'culturally safe' place, particularly for those students in Christchurch who challenge the city's reputation as a culturally homogeneous space and are thus frequently open to discrimination. A case study focusing on Somali refugee adolescents highlights that Christchurch's secondary schools – like those elsewhere in New Zealand – are not a culturally safe, certain space for all students but rather spatially reconstruct inequalities of gender, class and ethnicity. Yet, Somali students are not passive victims, for they have actively renegotiated these spaces within their schools with varying degrees of success.     

Religion on the Fringe: the representation of space and minority religious facilities in the rural–urban fringe of metropolitan Sydney,Australia

This article examines land-use development applications for minority religious facilities in two local government areas on the rural–urban fringe of metropolitan Sydney, Australia. Using critical discourse analysis and underpinned by Lefebvre's (1991) conceptual triad of space, the work interrogates the way in which place identity is generated and codified both by land-use planners and local residents through spatial representation. This representation is revealed in discourses around the compatibility of minority religious facilities for particular zones, lack of a sufficient minority population and social disruption. These discourses reveal a construction of peri-urban space that is aligned with particular elite Anglo-Australian activities (horse riding and gentleman farms) and land uses (rural residential, small-scale agriculture and the ‘bush-church’). These case studies illustrate the potential for the creation of exclusionary, abstract space by urban planners but also the ways in which local residents use discoursive strategies to ensure the stability of their position as elites in rapidly changing spatial situations.     

Uncertainty analysis of space–time prisms based on the moment-design method

Space–time prism (STP) is an important concept for the modeling of object movements in space and time. An STP can be conceptualized as the result of the potential path of a moving object revolving around in the three-dimensional space. Though the concept has found applications in time geography, research on the analysis and propagation of uncertainty in STPs, particularly under high degree of nonlinearity, is scanty. Based on the efficiency and effectiveness of the moment-design (M-D) method, this paper proposes an approach to deal with nonlinear error propagation problems in the potential path areas (PPAs) of STPs and their intersections. Propagation of errors to the PPA and its boundary, and to the intersection of two PPAs is investigated. Performance of the proposed method is evaluated via a series of experimental studies. In comparison with the Monte Carlo method and the implicit function method, simulation results show the advantages of the M-D method in the analysis of error propagation in STPs.     

Green infrastructure,green space,and sustainable urbanism: geography’s important role

ABSTRACT

This paper is a broad review of green infrastructure theory and practice relative to urban sustainability and the space for geographers in these discussions. We use examples from various urban sustainability plans to highlight ways in which green infrastructure is being conceptualized and implemented. We explore how geography contributes research on green infrastructure as well as the emerging practices as seen within sustainability plans. We identify four areas in which geographers can influence both green infrastructure theory and practice: 1) scale; 2) mapping distribution; 3) sensitivity to place and locale; and 4) equity and access. We conclude that in these areas geographers have tremendous opportunity contribute more deliberately to sustainable urbanism.     

A green space between life and death – a case study of activities in Gamlebyen Cemetery in Oslo,Norway

The amount of green space in urban areas is shrinking. Densification and the introduction of new user groups in most cities today is leading to more intensive use of public spaces and the need for more space. Urban cemeteries constitute a unique type of public space: while some may consider them primarily religious and contemplative spaces, others see them as primarily recreational or as heritage sites steeped in history. The authors examine the extent to which the pressure on cities’ open green areas combined with influences from intercultural encounters is mirrored in the use and character of cemeteries today – exemplified by Gamlebyen Cemetery (Gamlebyen Gravlund) in Oslo, Norway. They used observations in combination with short semi-structured interviews with those using the cemetery. The findings of pilot study conducted in 2013 suggest that religious aspects played a minor role and that the recreational aspects were more important to most of the interviewees. Many of them considered that the cemetery provided a pleasant green walkway on their way to work, busses or city services. The cemetery offered a combination of calmness, an aesthetically pleasant environment, and ‘cultivated nature’ in an urban context, and was thus an arena that invited respect and esteem.     

Spatio-temporal pattern changes of land space in Hengduan Mountains during 1990–2015

Hengduan Mountains offer land space for a variety of ecological services. However, the sustainable development and management of land space has been challenged by increased human activities in recent years. This paper performs the spatial pattern analysis of the quantitative and structural changes of various landscapes at different altitudes, and uses the land use data in 1990, 2000, 2010 and 2015 to reveal how various land patterns have changed. The results show that, within the production-living-ecological space schema, the ecological space dominates Hengduan Mountains, while the production and living space was mainly distributed in south region. During 1990–2015, the production-living-ecological spatial changes had been gradually accelerated and the regional differences had become more prominent. The agricultural production space had continuously decreased by 1132.31 km², and the industrial and mining production space had rapidly increased by 281.4 km² during 1990–2015. The living space had steadily increased, and the ecological space had increased with fluctuations. The land space pattern in Hengduan Mountains was greatly restricted by the terrain, such as altitude and slope. The implementations of China Western Development Strategy and the Returning Farmland to Forest Program had favorably promoted the changes of land spatial pattern in Hengduan Mountains.     

Modeling uncertainty of moving objects on road networks via space–time prisms

Moving objects produce trajectories, which are typically observed in a finite sample of time‐stamped locations. Between sample points, we are uncertain about the moving objects's location. When we assume extra information about an object, for instance, a (possibly location‐dependent) speed limit, we can use space–time prisms to model the uncertainty of an object's location.

Until now, space–time prisms have been studied for unconstrained movement in the 2D plane. In this paper, we study space–time prisms for objects that are constrained to travel on a road network. Movement on a road network can be viewed as essentially one‐dimensional. We describe the geometry of a space–time prism on a road network and give an algorithm to compute and visualize space–time prisms. For experiments and illustration, we have implemented this algorithm in MATHEMATICA.

Furthermore, we study the alibi query, which asks whether two moving objects could have possibly met or not. This comes down to deciding if the chains of space–time prisms produced by these moving objects intersect. We give an efficient algorithm to answer the alibi query for moving objects on a road network. This algorithm also determines where and when two moving objects may have met.     

User fees and the permeability of public space at municipal pools and bathhouses in New York City, 1870 – present

This paper examines fees for access to New York City’s public swimming and bathing spaces from 1870 to the present. We argue that, beyond generating revenue and rationing space, charges for admission to public bathing spaces have served to condition how permeable those spaces were to various groups of potential users. Municipal actors involved in administering baths and pools have used fees to maintain and order these spaces; to distinguish between deserving and undeserving users; and to include and exclude participants in an ostensibly universal public. Over time, fees have been naturalized, erasing these motivations and giving cause to their outcomes. We problematize the fee in order to address both theoretical questions about the nature of public space and practical ones about how municipal administrators govern amidst competing pressures to serve, develop and regulate urban residents and their communities.     

Spatial chromatic model in high-dimensional spaces and the uniqueness of chromatic code: a new perspective of geographic entity–space relationship

The abstraction, representation, and computation of entity–space relationship are keystones of geographic information science (GIS). The newly proposed spatial chromatic tessellation (SCT) provides a novel model to explore this relationship. SCT has demonstrated a variety of potential applications in GIS, such as reasoning spatial topology, point pattern analysis, and Voronoi diagrams. This study aims to theoretically investigate SCT by focusing on two aspects: (1) extending SCT to higher dimensional spaces. Results show that cells missing in lower dimensional spaces are hidden in higher dimensional spaces; (2) exploring the uniqueness of chromatic codes, particularly the chromatic codes of 2-cell and 3-cell clusters: their codes are proved to be unique. In a mathematical perspective, the observed phenomena from the above two aspects bring some new thoughts into the first law of geography and spatial heterogeneity. Based on these new understandings of entity–space relationship, SCT is replaced by spatial chromatic model (SCM) in which spaces are created by entities themselves rather than by partitioning the space preexisted. This makes a change from an absolute geographic space to a relative geographic space.     

A network Kernel Density Estimation for linear features in space–time analysis of big trace data

ABSTRACT

Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.     

The role of visualisation in the choice of stationary non-separable space–time covariance functions: an application to air pollution data

Modelling spatio-temporal dependencies resulting from dynamic processes that evolve in both space and time is essential in many scientific fields. Spatio-temporal Kriging is one of the space–time procedures, which has progressed the most over the last few years. Kriging predictions strongly depend on the covariance function associated with the stochastic process under study. Therefore, the choice of such a covariance function, which is usually based on empirical covariance, is a core aspect in the prediction procedure. As the empirical covariance is not necessarily a permissible covariance function, it is necessary to fit a valid covariance model. Due to the complexity of these valid models in the spatio-temporal case, visualising them is of great help, at least when selecting the set of candidate models to represent the spatio-temporal dependencies suggested by the empirical covariogram. We focus on the visualisation of the most interesting stationary non-separable covariance functions and how they change as their main parameters take different values. We wrote a specialised code for visualisation purposes. In order to illustrate the usefulness of visualisation when choosing the appropriate non-separable spatio-temporal covariance model, we focus on an important pollution problem, namely the levels of carbon monoxide, in the city of Madrid, Spain.     

Exploring space–time paths in physical and social closeness spaces: a space–time GIS approach

Exploring the evolution of people’s social interactions along with their changing physical locations can help to achieve a better understanding of the processes that generate the relationships between physical distance and social interactions, which can benefit broad fields of study related to social networks. However, few studies have examined the evolving relationships between physical movements and social closeness evolution. This is partially related to the shortage of longitudinal data in both physical locations and social interactions and the lack of an exploratory analysis environment capable of effectively investigating such a process over space and time. With the increasing availability of sociospatiotemporal data in recent years, it is now feasible to examine the relationships between physical separation and social interactions at the individual level in a space–time context. This research was intended to offer a spatiotemporal exploratory analysis approach to address this challenge. The first step was to propose the concept of a social closeness space–time path, which is an extension of the space–time path concept in time geography, to represent evolving human relationships in a social closeness space. A space–time geographical information system (GIS) prototype was then designed to support the representation and analysis of space–time paths in both physical and social closeness spaces. Finally, the effectiveness of the proposed concept and design in gaining insight into the impact of physical migration on online social closeness was demonstrated through an empirical study. The contributions of this study include an extension of the time–geographic framework from physical space to social closeness space, the development of a multirepresentation approach in a GIS to integrate an individual’s space–time paths in both physical and social closeness spaces, and an exploratory analysis of the evolving relationships between physical separation and social closeness over time.     

An ensemble approach to space–time interpolation

The availability of spatial data on an unprecedented scale as well as advancements in analytical and visualization techniques gives researchers the opportunity to study complex problems over large urban and regional areas. Nevertheless, few individual data sets exist that provide both the requisite spatial and/or temporal observational frequency to truly facilitate detailed investigations. Some data are collected frequently over time but only at a few geographic locations (e.g., weather stations). Similarly, other data are collected with a high level of spatial resolution but not at regular or frequent time intervals (e.g., satellite data). The purpose of this article is to present an interpolation approach that leverages the relative temporal richness of one data set with the relative spatial richness of another to fill in the gaps. Because different interpolation techniques are more appropriate than others for specific types of data, we propose a space–time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem to increase the accuracy results.

We call our ensemble approach the space–time interpolation environment (STIE). The primary steps within this environment include a spatial interpolation processor, a temporal interpolation processor, and a calibration processor, which enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In this article, we first describe STIE conceptually including the data input requirements, output structure, details of the primary steps, and the mechanism for coordinating the data within those steps. We then describe a case study focusing on urban land cover in Phoenix, Arizona, using our working implementation. Our empirical results show that our approach increased the accuracy for estimating urban land cover better than a single interpolation technique.     

Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin,Paris, and Tallinn

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.