Extracting and comparing places using geo-social media

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places

Metrics and methods for social distance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-189).Distance measures are important for scientists because they illustrate the dynamics of geospatial topologies for physical and social processes. Two major types of distance are generally used for this purpose: Euclidean Distance measures the geodesic dispersion between fixed locations and Cost Distance characterizes the ease of travel between two places. This dissertation suggests that close inter-place ties may be an effect of human decisions and relationships and so embraces a third tier of distance, Social Distance, as the conceptual or physical connectivity between two places as measured by the relative or absolute frequency, volume or intensity of agent-based choices to travel, communicate or relate from one distinct place to another. In the spatial realm, Social Distance measures have not been widely developed, and since the concept is relatively new, Chapter 1 introduces and defines geo-contextual Social Distance, its operationalization, and its novelty. With similar intentions, Chapter 2 outlines the challenges facing the integration of social flow data into the Geographic Information community. The body of this dissertation consists of three separate case studies in Chapters 3, 4 and 5 whose common theme is the integration of Social Distance as models of social processes in geographic space. Each chapter addresses one aspect of this topic. Chapter 3 looks at a new visualization and classification method, called Weighted Radial Variation, for flow datasets. U.S. Migration data at the county level for 2008 is used for this case study. Chapter 4 discusses a new computational method for predicting geospatial interaction, based on social theory of trip chaining and communication. U.S. Flight, Trip and Migration data for the years 1995-2008 are used in this study. Chapter 5 presents the results of the tandem analysis for social networks and geographic clustering. Roll call vote data for the U.S. House of Representatives in the 111th Congress are used to create a social network, which is then analyzed with regards to the geographic districts of each congressperson.by Clio Andris.Ph.D

A Markov Chain Random Field Cosimulation-Based Approach for Land Cover Post-classification and Urban Growth Detection

The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet

BEYOND HUMAN FACTORS : EXAMINING THE UNDERLYING DETERMINANTS OF RECREATIONAL BOATING ACCIDENTS WITH SPATIAL ANALYSIS AND MODELING

Recreational boating has grown in popularity in recent decades, accompanied with increased accidents resulting in property damage and personal injury. Some 5,000 recreational boating accidents are reported annually, ranking recreational boating as a leading cause of transportation accidents, second only to automotive. Recent research suggests that recreational boating accidents stem from multiple factors. In contrast, public perception and public policy overwhelmingly attribute boating accidents to human error, e.g., operator drug or alcohol use or lack of experience. This dissertation offers a comprehensive perspective on recreational boating accidents by exploring human, technological, and environmental factors that most influence these accidents. This level of inclusiveness is absent from previous research. The conceptual model developed in this dissertation is derived from general accident theory that integrates spatial and temporal qualities of recreational boating (and boating accidents) from satellite imagery, on-the-water boater surveys, and federal boating accident data. Data were assembled for two distinctive research sites, Sandusky, OH and Tampa, FL. Analyses of these data depended, in part, upon various forms of spatial statistics, e.g., hot spot analyses. The boating accident model developed here uses the multivariate negative binomial model to analyze accident count data aggregated to 0.25 mi² grid cells. The result is a synthetic model with improved parameter estimates and predictive capability compared to previous boating accident research. Key risk factors contained in the final model clearly represent human (operator experience), technological (boat speed and length), and environmental (boat density and channel character) dimensions. This research has important societal impact, i.e., to public officials faced with the allocation of limited resources. In particular, this research emphasizes the concentrated nature of boating risk in time (seasonality, day of week, time of day) and in space (shoals, channels, fixed facilities). These features should guide the timing and the placement of mobile law enforcement capacity as well as the location of operation centers near high risk boating sites. Finally, this work emphasizes the need for investigations of additional sites and the importance of including remotely sensed data to complement survey data in studies of recreational boating accidents.  Ph.D

Analysis and Geovisualisation of Hector’s Dolphin Abundance and Distribution Patterns in Space and Time

While Hector’s dolphin (Cephalorhynchus hectorii) has been the topic of many research projects within the first Marine Mammal Sanctuary in New Zealand, few long-term analytical abundance and distribution projects in other population strongholds have been conducted. The primary purpose of this thesis project was to test quantitative observations that suggested that this unprotected population of Hector’s dolphin at Te Waewae Bay, on the south coast of the South Island, New Zealand, may be in decline and utilises non-continuous portions of the coastline. Seasonal patterns of distribution and density were extracted from a rich data set collected over 24 consecutive months that provided fine-scale data of encounters with dolphins along four preplanned transects that followed the concave nature of the bay. Monthly data were binned into seasons producing eight seasons of data over the two years. Survey results revealed that Hector’s dolphin in warmer seasons were found in greater densities closer to shore and that in the cooler seasons the range extended outward and across more offshore areas. Individual seasons did not have as strong a pattern as the complete two year data set that indicated hotspots of higher densities of dolphins in the vicinity of freshwater inputs into Te Waewae Bay. To explore individual spatio-temporal movement patterns and how the individual patterns relate to group spatio-temporal patterns, 58 individual Hector’s dolphin movements were extracted from geo-tagged photographic data and then analysed. Visual analysis of movement patterns of individual dolphins were found to vary dramatically, having distribution patterns that exhibited a high degree of site fidelity. Most notable were the twenty one dolphins that remained in relatively small areas on either the east (ten dolphins) or west (eleven dolphins) halves of the bay. This evidence of strong site fidelity may suggest partitioning along as yet unidentified social or environmental parameters. Abundance estimates were calculated from mark-recapture photographic identifications. Calculations using Pollock’s Robust Design were limited to seasonal estimates of the total population of Hector’s dolphins, which ranged from the low in winter 2005 of 380 (CV=13%; 95% CI, 300-500) to the high in summer 2005/2006 of 580 (CV=9%; 95% CI, 480-700). The estimates from these eight seasons correspond to the numbers of dolphins that utilise the bay as their primary homerange and indicate that the population is not yet in a critical decline. However, caution is urged in interpretation because two years of field data is insufficient to calculate robust survival or reproduction rates for such a long lived species. To examine whether statistically quantifiable relationships exist between environmental variables and dolphin distribution patterns, both global (ordinary least squares; OLS) and local regression (geographically weighted regression; GWR) modelling techniques were applied. The local model was a spatially explicit model. The GWR model outperformed the OLS model, revealing statistically significant hotspots directly related to the amount of rain falling four days prior to the surveys being conducted as well as to distance from the main source of freshwater in the bay. The outcomes from this thesis offer a robust baseline of information regarding the population of Hector’s dolphin in Te Waewae Bay, such that management will have the capacity to monitor long term changes in abundance and distribution. This thesis findings have suggested that freshwater input may play a crucial role in Hector’s dolphin distribution in Te Waewae Bay, which when added to previous research results indicating the importance of oceanic frontal zones, water clarity, and depth, suggests that the picture of habitat requirements for Hector’s dolphin is becoming less obscure

Standard Interfaces and Protocols at Sensor Network and Cloud Level Definition

In this paper we presented full design of the system for monitoring forest which consists of cloud platform, sensor networks and mobile (drone) technologies for data collection and cameras. We first present the advanced design and structural model of an advanced system for monitoring of forest area. This model integrate sensor networks and mobile (drone) technologies for data collection and acquisition of those data at existing Crisis Management Information Systems (CMIS). Then we demonstrate the possibility to map different technological solutions and the main result was the definition of the set of standard interfaces and protocols for network interoperability

End-userApplication for Early Forest Fire Detection and Prevention

n this paper, we describe a Web application that has been designed and implemented by Fulda University of Applied Sciences in the context of the ASPires project. The application extends the functionality available to Crisis Management Centers (CMC). Actual readings from sensors installed in the test areas, for example national parks, are made available to CMC personnel, as well as pictures from cameras that are either mounted on stationary observation towers or taken by Unmanned Aerial Vehicles (UAVs) in the area of an actual of supposed forest fire. Data are transmitted to the Aspires cloud and delivered swiftly to the Web application via an open interface. Furthermore, fire alarms raised by novel detection algorithms are forwarded automatically to the application. This clearly improves the potential for the early detection of forest fires in rural areas

EXPLORING VOLUNTEERED GEOGRAPHIC INFORMATION WITH DATA QUALITY CONTROL FOR INTEGRATED PEST MANAGEMENT

Ph.DDOCTOR OF PHILOSOPH