Monitoring dynamics of urban landscape using spatial morphological indices: a case study of Thames Gateway area

Land use changes are results of interaction (over time and space) between humans and their physical environment. Cities and urban landscapes reflect the social, economic, political, environmental as well as technological processes in their changes as evident in their pattern and structures. This study tests the use of morphological indices for monitoring landscapes in a heavily modified landscape (urban). The study analyses the spatial and temporal changes in land use and land cover pattern in the area adjoining the Thames Gateway and selected parts of Greater London, UK. The investigation focuses on an examination of the temporal changes of various land use types as well as their structural properties and distribution over this period

Automated updating of road network databases: road segment grouping using snap-drift neural network

Presented in this paper is a major step towards an innovative solution of GIS road network databases updating which moves away from existing traditional methods where vendors of road network databases go through the time consuming and logistically challenging process of driving along roads to register changes or GIS road network update methods that are exclusively tied to remote sensing images. Our proposed road database update solution would allow users of GIS road network dependent applications (e.g. in-car navigation system) to passively collect characteristics of any “unknown route” (roads not in the database) on behalf of the provider. These data are transferred back to the provider and inputted into an artificial neural net (ANN) which decides, along with similar track data provided by other service users, whether to automatically update (add) the “unknown road” to the road database on probation allowing subsequent users to see the road on their system and use it if need be. At a later stage when there is enough certainty on road geometry and other characteristics the probationary flag could be lifted and permanently added to the road network database. Towards this novel approach we mimicked two journey scenarios covering two test sites and aimed to group the road segments from the journey into their respective road types using the snap-drift neural network (SDNN). The performance of the SDNN is presented and its potential in the proposed solution is investigated

Ethnicity, religion, and residential segregation in London: evidence from a computational typology of minority communities

Within the context of the growing polarisation and fragmentation of the urban landscape, this paper presents a computational typology applicable to the study of minority communities, both ethnic and religious, which is useful in understanding their spatial distribution and juxtaposition at neighbourhood levels. The typology has been applied to multicultural London with the use of the 2001 Census, in which there were questions on ethnicity and religion. The landscape of religion is found to be more highly segregated in contrast to the landscape of ethnicity. Furthermore, on the basis of a preliminary analysis of indicator variables, minorities seem on aggregate to be in an improved situation given a level of residential segregation, with the exception of residents of segregated Asian – Bangladeshi areas for ethnicity and residents of segregated Muslim areas for religion. This questions the generally held view that segregation in a multicultural society is undesirable per se and suggests that a ‘one size fits all’ government policy towards residential segregation is insufficiently perceptive. The typology introduced here should facilitate a more critically informed approach to multiculturalism and the contemporary city.

Detecting Clusters in Spatially Repetitive Point Event Data Sets

Abstract: The analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots ’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards exact spatial repetitiveness. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the ‘hot spots’. Key-words: spatial clustering, point event data, spatial repetition, Geo-ProZone analysis, robust normalisation Résumé: L’analyse des événements ponctuels a une longue tradition. La recherche de concentrations dans les semis d'évènements ponctuels ont un intérêt particulier, et la détection de ces concentrations est au cœur du data mining spatial. Certains modèles d'événements ponctuels ont une proportion significative des données ayant une tendance vers la répétition spatiale exacte. Comme exemples on peu citer des crimes et des accidents de trafic. La superposition spatiale des événements ponctuels rend problématique beaucoup d'approches existantes pour détecter ces concentrations. Dans cet article une approche de résolution variable, des Geo-ProZones, est appliquée aux données de 1 Cybergeo: Revue européenne de géographie, N ° 387, 11 juillet 2007 cambriolage de residences montrant un niveau élevé de répétition spatiale. Ceci est couplé avec la normalisation robuste comme moyen de définir et de visualiser uniformément les concentrations. Mots clés: analyse spatiale de proximité, événement ponctuel, répétition spatiale, analyses Geo-ProZone, normalisation robuste 1

Data Quality Challenges in Large-Scale Cyber-Physical Systems: a Systematic Review

Cyber-physical systems (CPSs) are integrated systems engineered to combine computational control algorithms and physical components such as sensors and actuators, effectively using an embedded communication core. Smart cities can be viewed as large-scale, heterogeneous CPSs that utilise technologies like the Internet of Things (IoT), surveillance, social media, and others to make informed decisions and drive the innovations of automation in urban areas. Such systems incorporate multiple layers and complex structure of hardware, software, analytical algorithms, business knowledge and communication networks, and operate under noisy and dynamic conditions. Thus, large-scale CPSs are vulnerable to enormous technical and operational challenges that may compromise the quality of data of their applications and accordingly reduce the quality of their services. This paper presents a systematic literature review to investigate data quality challenges in smart-cities large-scale CPSs and to identify the most common techniques used to address these challenges. This systematic literature review showed that significant work had been conducted to address data quality management challenges in smart cities, large-scale CPS applications. However, still, more is required to provide a practical, comprehensive data quality management solution to detect errors in sensor nodes’ measurements associated with the main data quality dimensions of accuracy, timeliness, completeness, and consistency. No systematic or generic approach was demonstrated for detecting sensor nodes and sensor node networks failures in large-scale CPS applications. Moreover, further research is required to address the challenges of ensuring the quality of the spatial and temporal contextual attributes of sensor nodes’ observations