Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Simulation of urban system evolution in a synergetic modelling framework. The case of Attica, Greece

Spatial analysis and evolution simulation of such complex and dynamic systems as modern urban areas could greatly benefit from the synergy of methods and techniques that constitute the core of the fields of Information Technology and Artificial Intelligence. Additionally, if during the decision making process, a consistent methodology is applied and assisted by a user-friendly interface, premium and pragmatic solution strategies can be tested and evaluated. In such a framework, this paper presents both a prototype Decision Support System and a consorting spatio-temporal methodology, for modelling urban growth. Its main focus is on the analysis of current trends, the detection of the factors that mostly affect the evolution process and the examination of user-defined hypotheses regarding future states of the problem environment. According to the approach, a neural network model is formulated for a specific time intervals and each different group of spatial units, mainly based to the degree of their contiguity and spatial interaction. At this stage, fuzzy logic provides a precise image of spatial entities, further exploited in a twofold way. First, for the analysis and interpretation of up-to-date urban evolution and second, for the formulation of a robust spatial simulation model. It should be stressed, however, that the neural network model is not solely used to define future urban images, but also to evaluate the degree of influence that each variable as a significant of problem parameter, contributes to the final result. Thus, the formulation and the analysis of alternative planning scenarios are assisted. Both the proposed methodological framework and the prototype Decision Support System are utilized during the study of Attica, Greece?s principal prefecture and the definition of a twenty-year forecast. The variables considered and projected refer to population data derived from the 1961-1991 censuses and building uses aggregated in ten different categories. The final results are visualised through thematic maps in a GIS environment. Finally, the performance of the methodology is evaluated as well as directions for further improvements and enhancements are outlined. Keywords: Computational geography, Spatial modelling, Neural network models, Fuzzy logic.

Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

Air pollution Analysis with a PFCM Clustering Algorithm Applied in a Real Database of Salamanca (Mexico)

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>. \<10.3389/fspas.2015.00003 \&g

Deriving Supply-side Variables to Extend Geodemographic Classification

The traditional proprietary geodemographic information systems that are on the market today use well-established methodologies. Demographic indicators are selected as a proxy for affluence and are then often linked to customer databases to derive a measure of the level of consumption expected from the different area typologies. However, these systems ignore fundamental relationships in the retail market by focusing upon demand characteristics in a ‘vacuum’ and ignore the supply side and consumer-supplier interaction. This paper argues that there may be considerable advantages to including supply-side indicators within geodemographic systems. Whilst the term ‘supply’ in this context might imply the number of consumer services already in an area, equally important for understanding demand are variables such as the supply of jobs and houses. We suggest that profiling an area in terms of its labour market characteristics gives a better insight into the income chain while the supply of houses could be argued to be a crucial factor in household formation that in turn will impact upon demographic structure. Using the regional example of Yorkshire and Humberside in northern England, we indicate how a suite of supply-side variables relating to the labour market can be assembled and used alongside a suite of demand variables to generate a new area classification. Spatial interaction models are calibrated to derive some of the variables that take into account zonal self-containment and catchment size

Flow time series clustering for demand pattern recognition in drinking water distribution systems: New insights about the most adequate methods

This study presents a proposal of clustering methodologies for demand pattern recognition using network flow data collected from a large set of drinking water distribution networks in Portugal. Most of the existing studies about clustering in flow time series rely on hierarchical or k-Means clustering algorithms with inelastic measures distances. This study explores alternative clustering algorithms, distance measures, comparison time windows, internal index metrics and clustering prototypes. The performance of the alternative clustering methodology was assessed in terms of multiple internal index metrics and the characterization of the cluster centroids. The methods with the best performance were Partition Algorithm with DTW distance, PAM prototype with 15 minutes time window and the Partition Algorithm with GAK distance, PAM prototype and 15 minutes time window because they allow a clear partition of flow time series in three clusters. The first method identifies a night consumption pattern, a typical weekend pattern and a typical working day pattern, whereas the second one identifies a pattern with small variability between night and daily consumption. To improve knowledge extraction, in terms of typical and anomalous existing patterns, additional clustering operations were performed with the flow data set that belongs to the cluster with small variability between night and daily consumption. New clusters were identified and characterized regarding weekday, geographical location, and dry months and wet months, showing that patterns associated with garden irrigation are independent of the period of the day and season of the year, which indicates an inefficient water use.Este estudo apresenta uma proposta de metodologias de clustering para reconhecimento de padrões de consumo usando um conjunto de dados de caudal coletados em redes de distribuição de água em Portugal. A maioria dos estudos existentes sobre clustering em séries temporais de caudal baseia-se em algoritmos de clustering hierárquicos ou de k-Means com medidas de distâncias inelásticas. Este estudo explora alternativas de algoritmos de clustering, medidas de distância, janelas temporais de comparação, medidas de índice interno e protótipos de clustering. O desempenho das metodologias de clustering foi avaliado em termos de medidas de índice interno e também através da caracterização dos centroides dos clusters. As metodologias com melhor desempenho foram o Algoritmo de Partição com distância DTW, protótipo PAM e janela de temporal de 15 minutos e o Algoritmo de Partição com distância GAK, protótipo PAM e janela de temporal de 15 minutos, pois permitiram a formação três clusters. O primeiro método identifica um padrão de consumo noturno, um padrão típico de fim-de-semana e um padrão típico de dia útil, enquanto o segundo método destaca-se por apresentar um padrão com pequena variabilidade entre o consumo noturno e diurno. Para melhorar a extração de conhecimento, operações adicionais de clustering foram realizadas ao conjunto de dados que pertence ao cluster com pequena variabilidade entre consumo noturno e diurno. Novos clusters foram identificados e caracterizados, mostrando que os padrões associados à irrigação são independentes do período do dia e da época do ano, o que indica um uso ineficiente da água