Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Comparative study on machine learning algorithms for early fire forest detection system using geodata

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps

Towards a model for the multidimensional analysis of field data

International audienceIntegration of spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Usually, SOLAP models exploit discrete spatial data. Few works integrate continuous field data into dimensions and measures. In this paper, we provide a multidimensional model that supports measures and dimension as continuous field data, independently of their implementation

Modeling and manipulating spacetime objects in a true 4D model

The concept of spacetime has long been used in physics to refer to models that integrate 3D space and time as a single 4D continuum. We argue in this paper that it is also advantageous to use this concept in a practical geographic context by realizing a true 4D model, where time is modeled and implemented as a dimension in the same manner as the three spatial dimensions. Within this paper we focus on 4D vector objects, which can be implemented using dimension-independent data structures such as generalized maps. A 4D vector model allows us to create and manipulate models with actual 4D objects and the topological relationships connecting them, all of which have a geometric interpretation and can be constructed, modified, and queried. In this paper we discuss where such a 4D model fits with respect to other spatiotemporal modeling approaches, and we show concretely how higher-dimensional modeling can be used to represent such 4D objects and topological relationships. In addition, we explain how the 4D objects in such a system can be created and manipulated using a small set of implementable operations, which use simple 3D space and 1D time inputs for intuitiveness and which modify the underlying 4D model indirectly

SST: Integrated Fluorocarbon Microsensor System Using Catalytic Modification

Selective, sensitive, and reliable sensors are urgently needed to detect air-borne halogenated volatile organic compounds (VOCs). This broad class of compounds includes chlorine, fluorine, bromine, and iodine containing hydrocarbons used as solvents, refrigerants, herbicides, and more recently as chemical warfare agents (CWAs). It is important to be able to detect very low concentrations of halocarbon solvents and insecticides because of their acute health effects even in very low concentrations. For instance, the nerve agent sarin (isopropyl methylphosphonofluoridate), first developed as an insecticide by German chemists in 1938, is so toxic that a ten minute exposure at an airborne concentration of only 65 parts per billion (ppb) can be fatal. Sarin became a household term when religious cult members on Tokyo subway trains poisoned over 5,500 people, killing 12. Sarin and other CWAs remain a significant threat to the health and safety of the general public. The goal of this project is to design a sensor system to detect and identify the composition and concentration of fluorinated VOCs. The system should be small, robust, compatible with metal oxide semiconductor (MOS) technology, cheap, if produced in large scale, and has the potential to be versatile in terms of low power consumption, detection of other gases, and integration in a portable system. The proposed VOC sensor system has three major elements that will be integrated into a microreactor flow cell: a temperature-programmable microhotplate array/reactor system which serves as the basic sensor platform; an innovative acoustic wave sensor, which detects material removal (instead of deposition) to verify and quantify the presence of fluorine; and an intelligent method, support vector machines, that will analyze the complex and high dimensional data furnished by the sensor system. The superior and complementary aspects of the three elements will be carefully integrated to create a system which is more sensitive and selective than other CWA detection systems that are commercially available or described in the research literature. While our sensor system will be developed to detect fluorinated VOCs, it can be adapted for other applications in which a target analyte can be catalytically converted for selective detection. Therefore, this investigation will examine the relationships between individual sensor element performance and joint sensor platform performance, integrated with state-of-the-art data analysis techniques. During development of the sensor system, the investigators will consider traditional reactor design concepts such as mass transfer and residence time effects, and will apply them to the emerging field of microsystems. The proposed research will provide the fundamental basis and understanding for examining multifunctional sensor platforms designed to provide extreme selectivity to targeted molecules. The project will involve interdisciplinary researchers and students and will connect to K-12 and RET programs for underrepresented students from rural areas

Development of a GIS-based method for sensor network deployment and coverage optimization

Au cours des dernières années, les réseaux de capteurs ont été de plus en plus utilisés dans différents contextes d’application allant de la surveillance de l’environnement au suivi des objets en mouvement, au développement des villes intelligentes et aux systèmes de transport intelligent, etc. Un réseau de capteurs est généralement constitué de nombreux dispositifs sans fil déployés dans une région d'intérêt. Une question fondamentale dans un réseau de capteurs est l'optimisation de sa couverture spatiale. La complexité de l'environnement de détection avec la présence de divers obstacles empêche la couverture optimale de plusieurs zones. Par conséquent, la position du capteur affecte la façon dont une région est couverte ainsi que le coût de construction du réseau. Pour un déploiement efficace d'un réseau de capteurs, plusieurs algorithmes d'optimisation ont été développés et appliqués au cours des dernières années. La plupart de ces algorithmes reposent souvent sur des modèles de capteurs et de réseaux simplifiés. En outre, ils ne considèrent pas certaines informations spatiales de l'environnement comme les modèles numériques de terrain, les infrastructures construites humaines et la présence de divers obstacles dans le processus d'optimisation. L'objectif global de cette thèse est d'améliorer les processus de déploiement des capteurs en intégrant des informations et des connaissances géospatiales dans les algorithmes d'optimisation. Pour ce faire, trois objectifs spécifiques sont définis. Tout d'abord, un cadre conceptuel est développé pour l'intégration de l'information contextuelle dans les processus de déploiement des réseaux de capteurs. Ensuite, sur la base du cadre proposé, un algorithme d'optimisation sensible au contexte local est développé. L'approche élargie est un algorithme local générique pour le déploiement du capteur qui a la capacité de prendre en considération de l'information spatiale, temporelle et thématique dans différents contextes d'applications. Ensuite, l'analyse de l'évaluation de la précision et de la propagation d'erreurs est effectuée afin de déterminer l'impact de l'exactitude des informations contextuelles sur la méthode d'optimisation du réseau de capteurs proposée. Dans cette thèse, l'information contextuelle a été intégrée aux méthodes d'optimisation locales pour le déploiement de réseaux de capteurs. L'algorithme développé est basé sur le diagramme de Voronoï pour la modélisation et la représentation de la structure géométrique des réseaux de capteurs. Dans l'approche proposée, les capteurs change leur emplacement en fonction des informations contextuelles locales (l'environnement physique, les informations de réseau et les caractéristiques des capteurs) visant à améliorer la couverture du réseau. La méthode proposée est implémentée dans MATLAB et est testée avec plusieurs jeux de données obtenus à partir des bases de données spatiales de la ville de Québec. Les résultats obtenus à partir de différentes études de cas montrent l'efficacité de notre approche.In recent years, sensor networks have been increasingly used for different applications ranging from environmental monitoring, tracking of moving objects, development of smart cities and smart transportation system, etc. A sensor network usually consists of numerous wireless devices deployed in a region of interest. A fundamental issue in a sensor network is the optimization of its spatial coverage. The complexity of the sensing environment with the presence of diverse obstacles results in several uncovered areas. Consequently, sensor placement affects how well a region is covered by sensors as well as the cost for constructing the network. For efficient deployment of a sensor network, several optimization algorithms are developed and applied in recent years. Most of these algorithms often rely on oversimplified sensor and network models. In addition, they do not consider spatial environmental information such as terrain models, human built infrastructures, and the presence of diverse obstacles in the optimization process. The global objective of this thesis is to improve sensor deployment processes by integrating geospatial information and knowledge in optimization algorithms. To achieve this objective three specific objectives are defined. First, a conceptual framework is developed for the integration of contextual information in sensor network deployment processes. Then, a local context-aware optimization algorithm is developed based on the proposed framework. The extended approach is a generic local algorithm for sensor deployment, which accepts spatial, temporal, and thematic contextual information in different situations. Next, an accuracy assessment and error propagation analysis is conducted to determine the impact of the accuracy of contextual information on the proposed sensor network optimization method. In this thesis, the contextual information has been integrated in to the local optimization methods for sensor network deployment. The extended algorithm is developed based on point Voronoi diagram in order to represent geometrical structure of sensor networks. In the proposed approach sensors change their location based on local contextual information (physical environment, network information and sensor characteristics) aiming to enhance the network coverage. The proposed method is implemented in MATLAB and tested with several data sets obtained from Quebec City spatial database. Obtained results from different case studies show the effectiveness of our approach

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

Uncovering population dynamics using mobile phone data : the case of Helsinki Metropolitan Area

Understanding the whereabouts of people in time and space is necessary for unraveling how our societies function. Regardless, our understanding of human presence is predominantly based on static residential population data, which is often outdated and excludes certain population groups, such as commuters or tourists. In the light of development towards 24-hour societies and the needs for promoting sustainable and equitable urban planning, reliable data of population dynamics are needed. To this end, ubiquitous mobile phones provide an attractive source for estimating the spatiotemporal digital footprints of people. In this study, I set out to investigate 1) the feasibility of three different aggregated network-based mobile phone data – the number of voice calls, data transmission and general network connection attempts – as a proxy for human presence, 2) how does the population distribution vary in Helsinki Metropolitan Area over the course of a regular weekday and 3) the role of temporally-sensitive population data when analysing dynamic accessibility to grocery stores and transport hubs. To my best knowledge, this is the first attempt when mobile phone data is used to reveal population dynamics for scientific purposes in Finland. Mobile phone data collected by the mobile network operator Elisa in 2017–2018 and ancillary data about land cover, buildings and a time use survey were used to estimate the 24-hour population distribution of the Helsinki Metropolitan Area. The mobile phone data were allocated to statistical 250 m x 250 m grid cells using an advanced dasymetric interpolation method and validated against population register data from Statistics Finland. The resulting 24-hour population was used to map the pulse of the city and to introduce the first fully dynamic accessibility model in the study area. The results show that data use is a good proxy for people and outperforms voice calls or overall network connection attempts. During daytime, the static population overestimates the population in residential areas and underestimates the population in work and service areas. In general, the 24-hour population reveals the pulse of a city, which is highlighted especially in the inner city of Helsinki, where the relative share of population of the study area increases by 50 % from the share at night-time to its peak at noon. The results of the case study suggest that integrating dynamic population data to location-based accessibility analysis provides more realistic results compared to static population data, but the significance of dynamic population data depends on the study context and research questions. In summary, aggregated network-driven mobile phone data is a feasible alternative for dynamic population modelling, however, different mobile phone data types vary in representativeness, which should be taken into account when using mobile phone data in research. To this end, critical evaluation of data and transparent data description are essential. Overall, understanding 24-hour societies and supporting sustainable urban planning necessitates dynamic population data, but advancements in data policy and availability are needed to harvest these possibilities. The results of this study also provide new empirical insights of the population dynamics in the study area, which can be used to advance planning and decision making.Ymmärrys väestön alueellisen jakautumisen ajallisesta vaihtelusta on keskeistä yhteiskuntamme toiminnan ymmärtämiseksi. Tästä huolimatta ymmärrys ihmisten läsnäolosta on vähäistä ja perustuu pääasiassa staattisiin asuinpaikkakohtaisiin väestötietoihin, jotka ovat usein vanhentuneita ja saattavat johtaa eräiden väestöryhmien, kuten työmatkalaisten tai turistien, sivuuttamiseen. Kehityksen kohti ympärivuorokautista yhteiskuntaa ja kestävän ja tasa-arvoisen kaupunkisuunnittelun edistämisen tarpeiden valossa tarvitaan luotettavia tietoja väestön dynamiikasta. Tässä tutkimuksessa tarkastelin 1) kolmen eri verkkopohjaisen matkapuhelinaineiston – puheluiden, tiedonsiirtoyhteyksien ja verkkoyhteyksien muodostusyritysten lukumäärän – soveltuvuutta ihmisen läsnäolon kuvaajana, 2) miten väestöjakauma vaihtelee pääkaupunkiseudulla säännöllisen arkipäivän aikana ja 3) temporaalisten väestötietojen käytön roolia saavutettavuusmallinnuksessa tarkasteltaessa ruokakauppojen ja liikenteen solmukohtien saavutettavuutta joukkoliikenteellä. Parhaan tietämykseni mukaan tämä on ensimmäinen kerta, kun matkapuhelinaineistoja käytetään väestön dynamiikan tarkasteluun tieteellisiin tarkoituksiin Suomessa. Matkapuhelinoperaattori Elisan keräämiä matkapuhelinaineistoja (2017–2018) sekä aineistoja maankäytöstä, rakennuksista ja ajankäyttötutkimuksen tuloksia käytettiin pääkaupunkiseudun 24 tunnin väestöjakauman arvioimiseen. Matkapuhelimen tiedot allokoitiin 250 m x 250 m tilastoruutuihin käyttäen edistynyttä dasymetristä interpolointimenetelmää ja validoitiin Tilastokeskuksen väestörekisteritietoja käyttäen. Tuloksena saatua 24 tunnin väestöaineistoa käytettiin kaupungin pulssin analysointiin ja ensimmäisen täysin dynaamisen saavutettavuusmallin toteuttamiseen tutkimusalueella. Tutkimuksen tulokset osoittavat, että matkapuhelinten tiedonsiirto on hyvä kuvaaja ihmisten sijainnille ja parempi kuin puhelut tai verkkoyhteyksien muodostusyritykset. Päivän aikana staattinen väestöaineisto yliarvioi väestöä erityisesti asuinalueilla samalla aliarvioiden väestöä alueilla, joilla on työpaikka- tai palvelukeskittymiä. Yleisesti katsottuna 24 tunnin väestö paljastaa kaupungin pulssin, mikä korostuu erityisesti Helsingin keskustassa, jossa tutkimusalueen väestön suhteellinen osuus kasvaa 50 %:lla yöstä sen huippuun keskipäivällä. Tapaustutkimuksen tulokset havainnollistavat kuinka dynaamisen väestötietojen integroiminen sijaintipohjaiseen saavutettavuustarkasteluun tarjoaa realistisempia tuloksia verrattuna staattiseen väestöaineistoon, mutta dynaamisten väestötietojen integroimisen merkitys riippuu tutkimuksen kontekstista ja tutkimuskysymyksistä. Yhteenvetona voidaan todeta, että aggregoitu verkkopohjainen matkapuhelinaineisto on hyvä vaihtoehto dynaamisen väestön mallintamiseen, mutta soveltuvuus vaihtelee aineistojen välillä, mikä on tärkeä huomioida käytettäessä matkapuhelinaineistoja tutkimuksessa. Tätä vasten aineiston kriittinen tarkastelu ja läpinäkyvä aineiston dokumentointi on olennaista. Kaiken kaikkiaan 24 tunnin yhteiskuntien ymmärtäminen ja kestävän kaupunkisuunnittelun tukeminen edellyttävät dynaamisia väestötietoja, mutta tietopolitiikan ja aineistojen saatavuuden edistäminen on välttämätöntä tämän toteutumiseksi. Tämä työ tarjoaa myös uutta empiiristä tietoa väestön dynamiikasta pääkaupunkiseudulla, jota voidaan käyttää suunnittelun ja päätöksenteon tukena

Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain)

The livestock sector in the Valencian Community (Spain) has experienced an increase in the intensity of farming with an increase in the number and size of pig and poultry facilities. The absence of previous environmental requirements in this region has produced a high concentration of facilities in some areas, and urban sprawl has resulted in many farms located in problematic areas close to villages or towns, residential areas and protected areas. Conflicts surrounding land use and environmental issues have been a problem in the region for many years. The initial step to solve this problem is to produce a territorial planning system to intervene and correct the current development and adapt to new European environmental regulations. The objectives of this study are to group farms with homogeneous characteristics in the Valencian Community and to characterise and search for spatial dependency patterns within the livestock sector. These objectives have the final aim of contributing basic scientific information to subsequent administrative planning decisions for livestock. This study presented methodology based on Geographic Information Systems and statistical methods for dividing livestock farms into zones and for characterising these areas. We obtained nineteen livestock geographical areas with unique characteristics (such as livestock species composition) and verified that these areas did not follow a spatial pattern.Calafat Marzal, MC.; Gallego Salguero, AC.; Quintanilla García, I. (2015). Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain). Computers and Electronics in Agriculture. 114:58-67. doi:10.1016/j.compag.2015.03.005586711