Identifying and comparing changes in the spatial concentrations of urban poverty and affluence: a case study of inner London

There is increasing interest, both academically and politically, in the spatial concentrations of poverty and affluence within cities. Although there has been a long history of research in this area, the majority of work has been concerned with poverty rather than affluence and has relied upon methods and measurement techniques that do not take into account the spatial concentrations of poverty. This research attempts to address this problem by measuring poverty and affluence using GIS and spatial statistical methods. By utilising contemporary census data and data collected by Charles Booth one hundred years ago, the research attempts to reveal the long-term changes in the spatial concentrations of poverty and affluence within inner London.     

The use of spatial analytical techniques to explore patterns of fire incidence: A South Wales case study

The application of mapping and spatial analytical techniques to explore geographical patterns of crime incidence is well established. In contrast, the analysis of operational incident data routinely collected by fire brigades has received relatively less research attention, certainly in the UK academic literature. The aim of this paper is to redress this balance through the application of spatial analytical techniques that permit an exploration of the spatial dynamics of fire incidents and their relationships with socio-economic variables. By examining patterns for different fire incident types, including household fires, vehicle fires, secondary fires and malicious false alarms in relation to 2001 Census of Population data for an area of South Wales, we demonstrate the potential of such techniques to reveal spatial patterns that may be worthy of further contextual study. Further research is needed to establish how transferable these findings are to other geographical settings and how replicable the findings are at different geographical scales. The paper concludes by drawing attention to the current gaps in knowledge in analysing trends in fire incidence and proposes an agenda to advance such research using spatial analytical techniques.     

Artificial immune systems applied to fault detection and isolation: A brief review of immune response-based approaches and a case study

This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems.     

Subtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case study

A case study including the discrimination of traffic accidents as accident free and accident cases on Konya-Afyonkarahisar highway in Turkey using the proposed hybrid method based on combining of a new data preprocessing method called subtractive clustering attribute weighting (SCAW) and classifier algorithms with the help of Geographical Information System (GIS) technology has been conducted. In order to improve the discrimination of classifier algorithms including artificial neural network (ANN), adaptive network based fuzzy inference system (ANFIS), support vector machine, and decision tree, using data preprocessing need in solution of these kinds of problems (traffic accident case study). So, we have proposed a novel data preprocessing method called subtractive clustering attribute weighting (SCAW) and combined with classifier algorithms. In this study, the experimental data has been obtained by means of using GIS. The obtained GIS attributes are day, temperature, humidity, weather conditions, and month of occurred accident. To evaluate the performance of the proposed hybrid method, the classification accuracy, sensitivity and specificity values have been used. The experimental obtained results are 53.93%, 52.25%, and 38.76% classification successes using alone ANN, ANFIS, and SVM with RBF kernel type, respectively. As for the proposed hybrid method, the classification accuracies of 67.98%, 70.22%, and 61.24% have been obtained using the combination of SCAW with ANN, the combination of SCAW with SVM (radial basis function (RBF) kernel type), and the combination of SCAW with ANFIS, respectively. The proposed SCAW method with the combination of classifier algorithms has been achieved the very promising results in the discrimination of traffic accidents.     

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999-2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based “mapping zones”. Over the course of 2¹/₂ field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework.     

The effects of computer simulation and animation (CSA) on students' cognitive processes: A comparative case study in an undergraduate engineering course

This study focuses on the investigation of the effects of computer simulation and animation (CSA) on students' cognitive processes in an undergraduate engineering course. The revised Bloom's taxonomy, which consists of six categories in the cognitive process domain, was employed in this study. Five of the six categories were investigated, including remember, understand, apply, analyze, and evaluate. Data were collected via a think‐aloud protocol involving two groups of student participants: One group learned a worked example problem with a CSA module, and the other group learned the same problem with traditional textbook‐style instruction. A new concept called frequency index was proposed for use in qualitative research that involves the quantitative comparison of the overall popularity of a particular mental activity performed by two groups of students. The results show that as compared to traditional textbook‐style instruction, CSA significantly increases students' activities in the understand category of the revised Bloom's taxonomy during learning and significantly increases students' activities in the understand, apply, analyse, and evaluate categories during subsequent problem‐solving. That learning via CSA has a profound impact on subsequent problem‐solving is attributed to intensive human–computer interactions built in the CSA learning module.     

Learning how to write effectively for academic journals: A case study investigating the design and development of a genre-based writing tutorial system

Genre-based writing instruction (GBWI) has been used for English journal paper writing both in classroom teaching and in the development of materials utilizing move analysis and corpus-based analysis. Some writing systems and tutorials have also been developed to improve the writing of non-native English speakers (NNES), as well as to assist academics and researchers for their publications. However, most of these systems had been developed for certain aspects of academic journal writing, such as organizing references, preventing plagiarism, or finding appropriate collocations. Accordingly, EJP-Write, a Chinese-interfaced writing system for English academic journal writing, was developed based on GBWI to teach and assist journal writing in a user-friendly environment.The present study aimed to investigate the perceived usefulness (content effectiveness) and perceived usability (system functionality) of EJP-Write, and identify other factors that might influence user attitudes and continued usage intention. Data was collected via questionnaire (N = 35) and structural equation modeling (SEM) was conducted for fitness estimation on the modified Technology Acceptance Model (TAM). Semi-structured interviews were also conducted to collect additional information for usefulness and usability evaluation (N = 14). Regarding perceived usefulness, the results show that participants felt the EJP-Write content was useful and effective in teaching genre and move structure because of the aid of various forms of support and examples such as phrase and paragraph templates. Additionally, the learning materials for verb tenses and citation formats were informative and practical for the participants to avoid grammatical and technical errors. However, the move structure provided was limited to the discipline of e-learning and education; thus, move analysis for different fields was suggested. Regarding perceived usability, the citation-related features in EJP-Write were particularly well-regarded. Participants also provided suggestions to improve online editing and outline developing features in the system. Factors found to influence user attitudes, and thus the intention of continued use, were usefulness and usability, while writing anxiety and personalization had less impact.Findings of the quantitative and qualitative data analysis in the study suggest that EJP-Write can play multiple roles inside or outside of the classroom, both as a platform integrating most features essential for journal paper writing, and as a teacher providing guidance and learning materials necessary for this specific genre. It is anticipated that this study will contribute to the knowledge base about both content and interface design for journal paper writing in the discipline of e-learning and education. For program designers of web-based writing tutorials, the involvement of users in the development of move structure could both strengthen various GBWI approaches and solve issues related to disciplinary differences.     

Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain)

The Ayora region, situated about 60 km southwest of the city of Valencia/Spain, was chosen to demonstrate pathways of characterizing fire events and post-fire succession in Mediterranean ecosystems using multi-temporal satellite imagery. A corresponding time series of 6 Landsat MSS, 13 Landsat-5 TM and 1 Landsat-7 ETM + images, covering the period 1975—2000, was processed to account for geometric and radiometric distortions as well as sensor calibration. Spectral Mixture Analysis was applied to derive estimates of photosynthetic active green vegetation cover as a primary indicator.A combination of pixel-based linear trend analysis and diachronic thresholding was employed to procure a fire perimeter data base and characterize post-fire dynamics based on a temporally stratified trend analysis. The results were integrated with auxiliary information to evaluate driving factors and further interpreted in relation to field-based information on plant communities and post-fire succession patterns.Reflecting typical recovery phases with an initial establishment of grasses and herbaceous species, followed by a gradual development of a shrub layer and the subsequent colonization of tree individuals, temporal trajectories could be described by exponential functions and were related to plot-based botanical information.     

Comment on “A direct approach with computerized symbolic computation for finding a series of traveling waves to nonlinear equations” by Engui Fan and H.H. Dai: [Comput. Phys. Commun. 153 (2003) 17]

Fan and Dai [Comput. Phys. Commun. 153 (2003) 17] have found a series of traveling wave solutions for nonlinear equations by applying a direct approach with computerized symbolic computations. They have claimed that the proposed method, in comparison with most existing symbolic computation methods such as a tanh method and Jacobi function method, not only give new and more general solutions, but also provides a guideline to classify various types of the solution according to some parameters. We show that the claims by Fan and Dai are wrong since some of the solutions do not satisfy the differential equation that they have adopted for the algebraic method.