Geodemographics,visualisation, and social networks in applied geography

This review begins by acknowledging the success of geodemographics as an important area of activity in applied geography. However, it then develops a critique of the conceptual and computational underpinnings of the approach, and argues that changes in data supply and online communication have rendered current practices obsolete. It presents elements of a new perspective, entailing: changes in the specification, estimation and testing of online geodemographic systems; adoption of consultative practices from online folksonomies; automated generation of pen portraits; and ‘on the fly’ visualisation of the outcome of geodemographic classifications.     

The diversity of diversity: a critique of geodemographic classification

We report on an examination of two geodemographic classification systems based on an analysis of 1991 census variables, for districts, wards and census enumeration districts in England and Wales. We also review the associations among the variables examined, the extent to which certain underlying components might account for the overall variation and the types of areas that are least typical. The results show that small areas are different in many different ways; a few dimensions cannot provide enough information to describe an area fully. Diversity on most scales remains even after geodemographicclassification, emphasizing the advantages of task-specific classification.     

Rough classification and accuracy assessment

In search for methods to handle imprecision in geographical information this paper explores the use of rough classification to represent uncertainty. Rough classification is based on rough set theory, where an uncertain set is specified by giving an upper and a lower approximation. Novel measures are presented to assess a single rough classification, to compare a rough classification to a crisp one and to compare two rough classifications. An extension to the error matrix paradigm is also presented, both for the rough-crisp and the roughrough cases. An experiment on vegetation and soil data demonstrates the viability of rough classification, comparing two incompatible vegetation classifications covering the same area. The potential uses of rough sets and rough classification are discussed and it is suggested that this approach should be further investigated as it can be used in a range of applications within geographic information science from data acquisition and analysis to metadata organization.     

Biosensing and geography: A mixed methods approach

Biosensing measures of physiological (somatic) response offer a potentially powerful tool for capturing people's subconscious reactions to environmental stimuli. Combining biosensing with other techniques allows insights to be generated not only about the intensity of somatic response but also, crucially, the underlying causes of that response. Despite pioneering work in this area, a rigorous, empirically-led analysis of biosensing measures in mixed methods research has hitherto been lacking. We address this lacuna through a case study of urban walking, comparing a field-based study (30 participants) with a virtual exercise undertaken in a lab (25 participants). Combining biosensing with data on environmental stimuli (recorded using video/GPS) and interviews, three analytical modes are examined: biosensing-led; environment-led; and thematic-led. The analysis shows how each dataset can add contextualizing information to significant phenomena observed in the others. We demonstrate, however, that biosensing measures become considerably more difficult to interpret beyond the controlled environment of the lab. The paper concludes that biosensing should be seen as a valuable measure in field studies, but one which requires careful interpretation through other datasets, being of limited usefulness and reliability taken alone.     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.     

An Object Extraction Approach for Impervious Surface Classification with Very-High-Resolution Imagery

Detailed land cover maps provide important information for research and decision-making but are often expensive to develop and can become outdated quickly. Widespread availability of aerial photography provides increased accessibility of high-resolution imagery and the potential to produce high-accuracy land cover classifications. However, these classifications often require expert knowledge and are time consuming. Our goal was to develop an efficient, accurate technique for classifying impervious surface in urbanizing Wake County, North Carolina. Using an iterative training technique, we classified 111 nonmosaicked, very-high-resolution images using the Feature Analyst software developed by Visual Learning Systems. Feature Analyst provides object extraction classifications by analyzing spatial context in relation to spectral data to classify high-resolution imagery. Our image classification results were 95 percent accurate in impervious surface extraction, with an overall total accuracy of 92 percent. Using this method, users with relatively limited geographic information system (GIS) training and modest budgets can produce highly accurate object-extracted classifications of impervious and pervious surface that are easily manipulated in a GIS.     

Valley-marginal sand dunes in the south-west Kalahari: their nature, classification and possible origins

This paper considers the nature, classification and possible origin of a variety of desert sand dunes which occur in association with ephemeral drainage features in the south-west Kalahari. The dune forms, which we term valley-marginal dunes, are found at the top of valley flanks, and are narrow, elongate forms with arcuate and straight planform elements which mirror the course of extant and ancient channels. Following a discussion of the major schemes used to classify different dune types, we describe the morphology and situation of these valley-marginal dunes. We suggest that whilst the basic morphometry of these dunes is similar to many other dune types, the topographic setting in which they occur is relatively unusual. We conclude that valley-marginal dunes are a previously unrecognized landform which cannot be fitted into current classificatory schemes. It is suggested that one reason for this may be the lack of an explicit temporal dimension in most dune classifications.     

西安城市扩张及其驱动力分析

随着人口的增长与经济的发展,尤其我国西部城市在西部大开发的背景下,大中城市的扩张十分明显,深刻地影响城市周边的生态环境。如何快速准确地获取城市扩张信息,并分析其驱动力机制,对于指导城市规划,优化西部生态环境与可持续发展都具有十分重要的意义。作者分别采用了监督分类法和归一化裸露指数 (NDBI) 法提取了西安市的城市边界信息,并对二者进行对比分析,认为监督分类法提取的城市边界信息较为准确。在此基础上通过对相关统计资料的分析,认为西安市城区快速扩张与西部大开发以来西安市固定资产投资额的大幅增加以及经济的快速发展有密切的关系,此外人口的增加、交通等基础设施的发展也是重要的驱动因素。     

Analyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia,Spain

Urban researchers and planners are often interested in understanding how economic activities are distributed in urban regions, what forces influence their special pattern and how urban structure and functions are mutually dependent. In this paper, we want to show how an algorithm for ranking the nodes in a network can be used to understand and visualize certain commercial activities of a city. The first part of the method consists of collecting real information about different types of commercial activities at each location in the urban network of the city of Murcia, Spain. Four clearly differentiated commercial activities are studied, such as restaurants and bars, shops, banks and supermarkets or department stores, but obviously we can study other. The information collected is then quantified by means of a data matrix, which is used as the basis for the implementation of a PageRank algorithm which produces a ranking of all the nodes in the network, according to their significance within it. Finally, we visualize the resulting classification using a colour scale that helps us to represent the business network.     

基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例

土地利用分类精度直接决定土地利用/土地覆被变化相关研究的准确性,而基于决策树的遥感影像分类是近年来提高土地利用分类精度的重要方法。QUEST决策树在影像解译和空间表达方面,运算速度和分类精度均优于普通CART等决策树方法。本文以云南丽江地区为例,应用QUEST决策树分类方法,对该地区的Landsat TM 5影像图进行分类,同时将地形因素、植被指数作为地学辅助数据的因子添加到分类波段中,进行不同特征融合,来处理目标类别间的非线性关系,该方法在处理图像理解知识方面具有更大的灵活性;同时与普通决策树分类法的遥感影像分类的结果相比较,Kappa系数值从原来的0.789提高到0.849.在地形复杂的山地地区,针对TM影像数据,选择基于QUEST决策树分类能够有效提高土地利用分类结果精度。     

Targeting seasonal climate forecasts for integration into household level decisions: the case of smallholder farmers in Lesotho

Seasonal climate forecasts have been promoted as a means to increase the resilience of marginal groups in Africa. The manifestations of this are still to be seen. This paper argues that successful dissemination and adoption of the forecast requires an in-depth profile of the characteristics and needs of user groups. The case study of a mountainous village in southern Lesotho is used to highlight the decisions which one group of marginal users – smallholder farmers – might make in response to the forecast. A participatory role-play exercise explores what information households presently receive and how new climate forecast information could be integrated into seasonal decision making. Results show that there are a number of low-input options available to these farmers for responding to the forecast. Adoption, however, is going to require repeated exposure to the forecast in conjunction with forecast development that is suited to users' needs. The case study is linked back to the larger scale by suggesting paths that seasonal climate forecast development could take if it is to contribute to improving livelihood sustainability among marginal groups.     

利用限制性评分方法确定土地农业适宜性等级──以澳大利亚新南维尔士州为例

根据土地适宜性评价的概念和世界粮农组织提出的土地适宜性等级的划分规范,以澳大利亚新南维尔士州为例阐述了利用限制性评分方法确定土地农业适宜性等级的原理。     

基于土地利用/覆被分类系统估算 碳储量的差异——以海南岛森林为例

土地利用/覆被分类系统是碳蓄积研究的依据,然而各种碳蓄积研究所采用的土地利用/覆被分类系统不尽相同。根据1993年海南林业资源二类调查资料,我们按照USGS土地利用/覆被、LCCS土地覆被和中国科学院土地资源三种分类系统所定义的类别进行分类并计算了各自的碳储量和碳密度,为碳储量的进一步精确估算和土地覆被分类系统研制提供重要的科学依据。结果发现,（1）不同的土地利用/覆被分类系统所对应的总碳储量以及平均碳密度都有了明显的差别。林业调查资料的植被分类、FAO 土地覆被分类系统(LCCS)、USGS 土地利用/土地覆被分类系统以及我国学者常用的中国科学院土地资源分类系统的碳蓄积量(Tg C)分别是28.98、28.71、21.04和21.04;碳密度(t C/ha)分别是31.24、30.95、22.68和22.68。（2）土地利用分类系统和土地覆被分类系统之间的结果具有较大差异,其碳储量相差7.67～7.94 Tg C,碳密度则相差8.27～8.56 t C/ha;差距在26.47%～37.74%之间。与其他学者研究结果比较发现,土地利用/覆被分类系统造成的碳蓄积差异的变化方向是不定的,取决于具体的分类系统和材积-生物量函数。不同土地利用/覆被分类系统对于植被划分的不同,导致了材积-生物量回归方程和类别面积的差异是造成碳蓄积和碳密度估算差异的根本原因。目前常用的土地利用/覆被分类系统在估算碳蓄积中存在一定问题,不适合于高精度的碳蓄积计算。体现地表植被生物量差异、植被叶型和外貌特征、种类及树龄差异等内涵的土地利用/覆被分类系统利于陆地碳循环研究的深化。     

Monitoring Temporal Changes in Multivariate Classifications

Multivariate classifications have been used extensively to make inductive generalizations about place or objects under study. If classifications are dynamic, changing as the variables that define them change, then these generalizations and the policies and strategies based on these generalizations may not hold over time. This paper presents a replicable method for monitoring temporal changes in a classification at the structural and group levels. The method is then illustrated in an empirical example using longitudinal data that relate to the economies of a selected set of geographic areas.     

The viability of the genetic algorithm geodemographic modelling in the case of Korean DIF zoning

Development impact fees (DIF) are used for the provision of public infrastructure services to adequately serve new developments. Korean Ministry of Land, Transport, and Maritime Affairs introduced the DIF zoning in 2008 and, like its US counterpart, it requires Korean localities to designate specific districts called ‘DIF zones’ based on the local population growth rate. This study examines the genetic algorithms as a method for DIF zoning-related geodemographic modelling using the Korean National Geographic Information Systems as property-level ancillary data. A borough of Hwasung City is taken as the case area since the city internally collected population data by ward-level enumeration areas for DIF zoning in 2008. The gridded population map is built from this source enumeration area population data to select the training dataset. The functional form of the genetic algorithm model has been formulated to have a hierarchical weighting system in which categorical weights of variable groups and individual weights of subordinate variables are sought bilaterally. The model is run by a carefully pretested set of reproductive plan parameters and the consequences are compared with the conventional regression models. It is found that the genetic algorithm solutions are quite comparable to the ones obtained by the regression methods, and it seemed, in this regard, worth adopting the two approaches simultaneously in a complementary manner to take unique advantages of each other either in facilitating the analysis processes or in obtaining more promising outputs for the DIF zoning as well as other geodemographic applications.     

地貌类型混合法研究

针对现有地貌分类法的不足提出地貌类型混合法。该方法将地貌类型划分为主体地貌和客体地貌,主体地貌是载体,客体地貌是被载体,把客体地貌混合表示在主体地貌之上,解决了现有地貌分类的缺陷。与形态成因分类法相比,该方法探索了地貌分类的新途径,为促进地貌制图的实用性、简洁性奠定了基础。     

Freshwater geographies: Experimenting with knowing and doing geography differently

The fair and effective governance of freshwater is an increasingly prominent issue in New Zealand. Emerging from a complex of cultural, economic and biophysical narratives, freshwater geographies are multiple, varied and increasingly acknowledged as worthy of interdisciplinary scrutiny. In this commentary, we reflect on a series of generative spaces that we – as group of postgraduate geographers (plus supporting staff) – created to engage with the multiplicity of freshwater meanings both within and beyond the academy. Through this evolving epistemic‐political project, we significantly reframed our own understandings about what freshwater ‘is’ and how it ought to be governed. By pursuing a deeper understanding of how the world gets made, we expand our ability to know and make it differently.     

Map Use by Adults with Visual Impairments

In this paper we summarize five experiments that were designed to investigate how tactile maps contribute to the cognitive maps of people with visual impairments. In two experiments we demonstrated that tactile maps can contribute to peoples' ability to learn a route through an unfamiliar area. From the results of two further studies, we argue that compared to people with sight, people with visual impairments have greater difficulty encoding information from a map. We considered this directly in a fifth experiment by investigating how people with visual impairments search and learn a map. We found that they used less efficient strategies than people with sight. We conclude that tactile maps are an important source of geographical information when people need to learn about new environments, but we also point out that to benefit most from tactile maps people need to encode the map information as effectively as possible. As people with visual impairments do not always use the most appropriate encoding strategies we suggest that tactile map users might benefit from greater experience and training in map encoding strategies.