Personal activity centres and geosocial data analysis: Combining big data with small data

Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city

Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users

With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geoprivacy of active Twitter users who frequently share geotagged tweets in their home and work locations. By analyzing over 38,000 geotagged tweets of 93 active Twitter users in three U.S. cities, the two-dimensional Gaussian masking technique with proper standard deviation settings is found to be more effective to protect user\u27s location privacy while sacrificing geospatial analytical resolution than the random perturbation masking method and the aggregation on traffic analysis zones. Furthermore, a three-dimensional theoretical framework considering privacy, analytics, and uncertainty factors simultaneously is proposed to assess geomasking techniques. Our research offers insights into geoprivacy concerns of social media users\u27 georeferenced data sharing for future development of location-based applications and services

SOCIAL MEDIA FOOTPRINTS OF PUBLIC PERCEPTION ON ENERGY ISSUES IN THE CONTERMINOUS UNITED STATES

Energy has been at the top of the national and global political agenda along with other concomitant challenges, such as poverty, disaster and climate change. Social perception on various energy issues, such as its availability, development and consumption deeply affect our energy future. This type of information is traditionally collected through structured energy surveys. However, these surveys are often subject to formidable costs and intensive labor, as well as a lack of temporal dimensions. Social media can provide a more cost-effective solution to collect massive amount of data on public opinions in a timely manner that may complement the survey. The purpose of this study is to use machine learning algorithms and social media conversations to characterize the spatiotemporal topics and social perception on different energy in terms of spatial and temporal dimensions. Text analysis algorithms, such as sentiment analysis and topic analysis, were employed to offer insights into the public attitudes and those prominent issues related to energy. The results show that the energy related public perceptions exhibited spatiotemporal dynamics. The study is expected to help inform decision making, formulate national energy policies, and update entrepreneurial energy development decisions

A Data-Driven Approach for Modeling Agents

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating a gap in the literature. This dissertation proposes a novel data-driven approach for modeling agents to bridge the research gap. The approach is composed of four detailed steps including data preparation, attribute model creation, behavior model creation, and integration. The connection between and within each step is established using data flow diagrams. The practicality of the approach is demonstrated with a human mobility model that uses millions of location footprints collected from social media. In this model, the generation of movement behavior is tested with five machine learning/statistical modeling techniques covering a large number of model/data configurations. Results show that Random Forest-based learning is the most effective for the mobility use case. Furthermore, agent attribute values are obtained/generated with machine learning and translational assignment techniques. The proposed approach is evaluated in two ways. First, the use case model is compared to another model which is developed using a state-of-the-art data-driven approach. The model’s prediction performance is comparable to the state-of-the-art model. The plausibility of behaviors and model structure in the use case model is found to be closer to real-world than the state-of-the-art model. This outcome indicates that the proposed approach produces realistic results. Second, a standard mobility dataset is used for driving the mobility model in place of social media data. Despite its small size, the data and model resembled the results gathered from the primary use case indicating the possibility of using different datasets with the proposed approach

Deep Learning-Based Spatio-Temporal Data Mining Using Multi-Source Geospatial Data

With the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data

Modelling socio-spatial dynamics from real-time data

This thesis introduces a framework for modelling the social dynamic of an urban landscape from multiple and disparate real-time datasets. It seeks to bridge the gap between artificial simulations of human behaviour and periodic real-world observations. The approach is data-intensive, adopting open-source programmatic and visual analytics. The result is a framework that can rapidly produce contextual insights from samples of real-world human activity – behavioural data traces. The framework can be adopted standalone or integrated with other models to produce a more comprehensive understanding of people-place experiences and how context affects behaviour. The research is interdisciplinary. It applies emerging techniques in cognitive and spatial data sciences to extract and analyse latent information from behavioural data traces located in space and time. Three sources are evaluated: mobile device connectivity to a public Wi-Fi network, readings emitted by an installed mobile app, and volunteered status updates. The outcome is a framework that can sample data about real-world activities at street-level and reveal contextual variations in people-place experiences, from cultural and seasonal conditions that create the ‘social heartbeat’ of a landscape to the arrhythmic impact of abnormal events. By continuously or frequently sampling reality, the framework can become self-calibrating, adapting to developments in land-use potential and cultural influences over time. It also enables ‘opportunistic’ geographic information science: the study of unexpected real-world phenomena as and when they occur. The novel contribution of this thesis is to demonstrate the need to improve understanding of and theories about human-environment interactions by incorporating context-specific learning into urban models of behaviour. The framework presents an alternative to abstract generalisations by revealing the variability of human behaviour in public open spaces, where conditions are uncertain and changeable. It offers the potential to create a closer representation of reality and anticipate or recommend behaviour change in response to conditions as they emerge

REMOTE SENSING METHODS FOR THE INVESTIGATION OF THE EVOLUTION AND DYNAMICS OF ALPINE LANDSCAPES

Whilst the effects of present-day climate change are apparent in many environmental systems, much less is known about its impact upon the geomorphic systems characteristic of Alpine environments. This is an important knowledge gap because of the potential vulnerability of Alpine landscapes. The gap exists for two primary reasons: (1) observing climate forcing is challenging because it is manifest over timescales of decades to centuries, over which timescale geomorphic data are commonly scarce; and (2) the geomorphic response of landscapes to climate change can be complex, reflecting both spatially differential sensitivities to climate forcing and the effects of landscape heritage associated with historical glacial activity. Nonetheless, there is a consensus in the scientific community about the potentially high sensitivity of Alpine regions to climate change, because of the vulnerability of permafrost, glacial and nival processes to changes in atmospheric temperature and precipitation and the large amount of sediment stored on the associated hillsides. One approach to addressing this knowledge gap is to harness the power of remote sensing. A number of active and passive remote sensing methods could be employed for the reconstruction and monitoring of both whole landscapes and individual landforms. This Thesis aims to use such approaches to quantify the geomorphic dynamics of high mountain areas at the timescale of decades and so in the context of recent and rapid climate warming. It does so recognizing that both endogenous (landscape legacy) and exogenous (climatic forcing) processes may matter. To support this primary aim, a secondary aim arises: the evaluation of the potential of a number of remote sensing techniques for landscape and landform monitoring at multiple temporal and spatial scales. Thus this Thesis also tests in an Alpine setting the geomorphological potential of photogrammetric methods, using both aerial and hand-held sensors and both traditional and the innovative Structure-from-Motion processing approaches, and Terrestrial Laser Scanner techniques. The Thesis shows that remote sensing approaches prove to be an advantageous approach for a number of scales of application. In particular, over large spatial extents and in the case of decadal scale climate forcing of Alpine landscapes, photogrammetry was found to be capable of quantifying process rates within the limits of detection determined by the resolution of historical imagery. The information unlocked from aerial archives reveals distinct geomorphic responses to cold and warm periods and to changes in rates of precipitation and snow cover. Nonetheless, whilst enhanced sediment production is observed locally, evidence suggest a weak propagation of climate change signals through the landscape due to impeded connection to the river system and/or sediment transport capacity limitation. -- Bien que les effets des changements climatiques actuels soient visibles dans de nombreux systèmes environnementaux, un manque de connaissances des impacts sur les paysages alpins persiste. Cette lacune existe pour deux raisons principales : (1) l'observation du forçage climatique représente un défi, car ses conséquences se manifestent sur des périodes de plusieurs décennies, voire des siècles, pour lesquels les données géomorphologiques sont généralement rares ; et (2) la réaction du paysage aux changements climatiques peut être complexe, reflétant à la fois des sensibilités différentes au niveau spatial et les effets du patrimoine paysager, comme par exemple son histoire glaciaire. Néanmoins, il existe un consensus dans la communauté scientifique à propos de la haute sensibilité potentielle des régions alpines au changement climatique, en raison de la vulnérabilité du pergélisol et des processus glaciaires et neigeux aux changements de température atmosphérique et des précipitations et en raison de la grande quantité de sédiments stockés sur les versants alpins. Une stratégie pour aborder ces problématiques s'appuie sur le potentiel de la télédétection. Une série de méthodes de télédétection active et passive peuvent être utilisées pour reconstruire et surveiller le paysage entier et les éléments individuels qui le composent. Cette thèse vise l'application de ces approches pour quantifier les dynamiques géomorphologiques des paysages de haute montagne à l'échelle des décennies, et donc dans le contexte du réchauffement climatique récent et actuel. Cela est mis en pratique par la reconnaissance de l'importance des processus endogènes (héritage du paysage) et exogènes (forçage climatique). Le soutien à cet objectif en soulève un deuxième : l'évaluation du potentiel d'un certain nombre de techniques de télédétection pour le monitorage du relief et de ses formes géomorphologiques à plusieurs échelles temporelles et spatiales. Ainsi, cette thèse teste le potentiel des méthodes de photogrammétrie, en utilisant à la fois des senseurs aéroportés et portatifs et des approches de traitements traditionnels et innovants, et du balayage laser terrestre pour la recherche géomorphologique alpine. Les résultats obtenus montrent que les approches de télédétection se révèlent avantageuses pour des nombreuses échelles d'application. En particulier, sur de grandes étendues spatiales et dans le contexte du forçage climatique du paysage alpin, la photogrammétrie aérienne d'archive se montre appropriée pour la quantification des taux des processus dans les limites de détection déterminées par la résolution des photographies historiques. Les résultats démontrent l'existence d'une réponse géomorphologique distincte pour des périodes froides ou chaudes, ainsi que selon les variations des taux de précipitations et de couverture de neige. Néanmoins, alors qu'une production accrue de sédiments est observée localement, des évidences suggèrent une faible propagation des signaux du changement climatique à travers le paysage. Les raisons semblent être une faible contribution des versants au réseau fluvial et/ou une capacité de transport des sédiments limitée. -- Obwohl die Auswirkungen des aktuellen Klimawandels in zahlreichen Umweltsystemen beobachtet wurden, sind die Kenntnisse dieser Auswirkungen auf alpine Landschaften immer noch ungenügend. Diese Lücke existiert aus folgenden Gründen: (1) Das Beobachten klimatischer Auswirkungen auf alpine geomorphologische Prozesse stellt eine grosse Herausforderung dar, da diese sich über eine Zeitspanne von mehreren Jahrzehnten bis Jahrhunderten bemerkbar machen können, für die meist nur wenige geomorphologische Daten zur Verfügung stehen. (2) Durch die unterschiedlichen Empfind- lichkeiten verschiedener geomorphologischer Landschaftselemente sowie durch den grossen Einfluss des landschaftlichen Erbes, wie zum Beispiel der historischen Gletschertätigkeit, reagieren alpine Landschaftsentwicklungsprozesse sehr komplex auf Veränderungen des Klimas. Nichtsdestotrotz, auf- grund der hohen Empfindlichkeit des Permafrosts und der Gletscher- und Schneeprozesse gegenüber Veränderungen der atmosphärischen Temperatur und der Niederschlagsmenge sowie der grossen Menge an Sedimenten die an den Alpenhängen abgelagert werden und wurden, herrscht in der wis- senschaftlichen Gemeinschaft ein breiter Konsens über die potentielle hohe Sensibilität der alpinen geomorphologischen Systeme in Bezug auf den zu erwartenden Klimawandel. Fernerkundung bietet ein hohes Potential, um die geomorphologische Sensibilität zu erkunden. Aktive und passive Fernerkundungsmethoden können genutzt werden, um gesamte Landschaften sowie ihre einzelnen geomorphologischen Elemente historisch zu rekonstruieren und kontinuierlich zu überwachen. Die vorliegende Dissertation zielt auf die Anwendung dieser Ansätze, um die geomorpho- logische Dynamik der hochalpinen Landschaft über Jahrzehnte, und somit im Kontext der jüngsten Klimaerwärmung, zu quantifizieren. Der hier dargestellte Ansatz fokussiert vor allem auf die Bedeutung der endogenen (landschaftliches Erbe) und exogenen (klimatische Einflüsse) Prozesse. Die Umsetzung dieses primären Ziels zieht ein sekundäres Ziel mit sich: Die Bewertung des Potenzials einer Reihe von Fernerkundungsmethoden für das Monitoring von Landschaften und ihrer geomorphologischen For- men auf mehreren rüumlichen und zeitlichen Skalen. Damit wird das Potenzial photogrammetrischer Methoden, insbesondere luftgestützter und tragbarer Sensoren in Kombination mit traditionellen und innovativen "Structure-from-Motion" Ansätzen, sowohl auch terrestrischen Laserscanning Techniken für die alpine geomorphologische Forschung getestet. Die Ergebnisse zeigen, dass die hier dargestellten Fernerkundungsansätze für eine breite Reihe von Anwendungsskalen vorteilhaft sind. Die Archiv-Luftphotogrammmetrie ist besonders für die Quan- tifizierung der Auswirkungen des Klimawandels auf geomorphologische Prozesse in grossen Land- schaftsausschnitten geeignet. Die Auflösung der historischen Luftbilder bestimmt die Detektionsgrenze dieser Prozesse. Die aus den Luftarchiven ermittelten Informationen zeigen, dass kalte und warme Klimaphasen, sowie Variationen der Niederschlagsmenge und der Schneedeckenmächtigkeit unter- schiedliche Auswirkungen auf geomorphologische Prozesse haben. Obwohl ein lokaler Anstieg der Sedimentproduktion beobachtet werden konnte, konnten nur geringe Anzeichen einer Ausbreitung dieser Klimawandelsignale in der Landschaft beobachtet werden. Die Gründe hierfür scheinen der geringe Beitrag der untersuchten Berghänge zum Gesamtwasserabfluss und/oder die beschränkte Sedimenttransportfähigkeit zu sein. -- Nonostante gli effetti del cambiamento climatico attuale siano evidenti in molti sistemi ambientali, una conoscenza deficitaria perdura riguardo il suo impatto sui paesaggi alpini. Tale lacuna esiste per due principali ragioni: (1) gli effetti del cambiamento climatico sono difficili da osservare, in quanto manifesti su scale temporali di decenni, o persino secoli, per le quali prevale una scarsità di dati geomorfologici esaustivi; e (2) la reazione del paesaggio a tali cambiamenti può essere complessa e riflettere al contempo delle sensibilità spaziali differenti e gli effetti del patrimonio paesaggistico, come ad esempio la cronistoria glaciale. Tuttavia, vi è un consenso nella comunità scientifica riguardo l'ele- vata sensibilità delle regioni alpine ai cambiamenti climatici, a causa della vulnerabilità di permafrost e processi glaciali e nevosi ai cambiamenti di temperatura atmosferica e di precipitazioni, oltre che all'ampio stoccaggio di sedimenti concentrato sui pendii alpini. Una strategia per colmare questa lacuna di conoscenza può essere l'avvalersi del potenziale delle tecniche di telerilevamento. Vari metodi di telerilevamento attivo e passivo possono essere impiegati per ricostruire e monitorare il paesaggio ed i singoli elementi che lo compongono. Questa tesi si propone di utilizzare tali metodi per quantificare le dinamiche geomorfologiche nelle regioni di alta montagna a scala temporale decennale, e quindi nel contesto del riscaldamento climatico recente e attuale. In tale approccio viene riconosciuta l'importanza dei processi di tipo endogeno (di eredità paesaggistica) ed exogeno (climatici). A sostegno di questo obiettivo primario, una seconda finalità si pone: lo sviluppo e la valutazione di diverse tecniche di telerilevamento per il monitoraggio dei rilievi alpini e delle loro forme geomorfologiche, a più scale temporali e spaziali. Pertanto, questa tesi mette alla prova metodi di fotogrammetria, utilizzando al contempo sensori aeroportati e portatili ed approcci tradizionali ed innovativi (come l'emergente Structure-from-Motion), e tecniche di scansione laser per la ricerca geomorfologica in scenari alpini. I risultati ottenuti dimostrano come gli approcci di telerilevamento rappresentino una risorsa efficace e vantaggiosa per una vasta gamma di applicazioni. In particolare, ad ampia scala spaziale e nel contesto di cambiamento climatico nelle regioni alpine, la fotogrammetria aerea d'archivio si è dimostrata appropriata per la quantificazione dei processi geomorfologici entro limiti di rilevamento determinati dalla risoluzione delle immagini storiche stesse. I risultati rivelano una reazione geomorfica distinta a periodi di caldo e freddo, oltre che a variazioni di precipitazioni e copertura nevosa. Ciononostante, malgrado un accrescimento della produzione sedimentaria sia presente a scala locale, la propagazione dei segnali di cambiamento climatico attraverso il paesaggio appare debole. La ragione risiede nello scarso contributo dei versanti al sistema fluviale e/o a limitate capacità di trasporto di sedimenti

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management