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1	Modeling space-time activities and places for a smart space —a semantic approach Fan, Junchuan 01 August 2017 (has links) The rapid advancement of information and communication technologies (ICT) has dramatically changed the way people conduct daily activities. One of the reasons for such advances is the pervasiveness of location-aware devices, and people’s ability to publish and receive information about their surrounding environment. The organization, integration, and analysis of these crowdsensed geographic information is an important task for GIScience research, especially for better understanding place characteristics as well as human activities and movement dynamics in different spaces. In this dissertation research, a semantic modeling and analytic framework based on semantic web technologies is designed to handle information related with human space-time activities (e.g., information about human activities, movement, and surrounding places) for a smart space. Domain ontology for space-time activities and places that captures the essential entities in a spatial domain, and the relationships among them. Based on the developed domain ontology, a Resource Description Framework (RDF) data model is proposed that integrates spatial, temporal and semantic dimensions of space-time activities and places. Three different types of scheduled space-time activities (SXTF, SFTX, SXTX) and their potential spatiotemporal interactions are formalized with OWL and SWRL rules. Using a university campus as an example spatial domain, a RDF knowledgebase is created that integrates scheduled course activities and tweet activities in the campus area. Human movement dynamics for the campus area is analyzed from spatial, temporal, and people’s perspectives using semantic query approach. The ontological knowledge in RDF knowledgebase is further fused with place affordance knowledge learned through training deep learning model on place review data. The integration of place affordance knowledge with people’s intended activities allows the semantic analytic framework to make more personalized location recommendations for people’s daily activities. Deep Learning Human mobility RDF Semantic Web Smart space Space-time Activity Geography
2	GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS Data Dalumpines, Ron 26 September 2014 (has links) Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use). The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit. There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD) GPS time use diary episode extraction multinomial logit travel behavior mode detection episode reconstruction GIS map-matching route choice path size logit potential path area scale estimation Python ArcGIS activity analysis trip reconstruction smartphone global positioning system geographic information system toolkit work trip shop trip potential activity location land use activity episode travel episode stop episode transferability scalability modularity scripting big data travel survey respondent burden preprocessing multipath error tracking purpose detection segmentation data filter data smoothing fuzzy logic neural network decision tree rule-based algorithm trajectory point road network network dataset gateway shortest path horizontal dilution of precision HDOP commonality factor mode transfer point Halifax Nova Scotia Space-Time Activity Research variance inflation factor shapefile comma-separated values traveling salesman problem data mining branch-and-bound algorithm pedestrian network alternative route observed route route efficiency route attributes distance time heading bearing duration acceleration latitude longitude coordinate overlay analysis intersect shopping module data logger walk likelihood ratio test classification table path generation kappa statistic degrees data collection transportation research automate framework ArcToolbox spatio-temporal navigation positioning trace path spatial data topology horizontal accuracy SPSS Stata spatial resolution temporal resolution household survey trip reporting proximity analysis DMTI Desktop Mapping Technologies Inc. road intersection route overlap left turn right turn location analysis time geography spatial statistics buffer analysis cycling bus transit public transportation GEOIDE AGILE data need raw data urban canyon endpoint data cleaning elevation satellite outliers automatic processing nearest node classifier classification method short trip multi-point

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Modeling space-time activities and places for a smart space —a semantic approach

GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS Data