Geocoding location expressions in Twitter messages: A preference learning method

Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors

The shocklet transform: a decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series

We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series—termed the Discrete Shocklet Transform (DST)—and an associated similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm, that indicates time windows during which panels of time series display qualitatively-similar anomalous behavior. After distinguishing our algorithms from other methods used in anomaly detection and time series similarity search, such as the matrix profile, seasonal-hybrid ESD, and discrete wavelet transform-based procedures, we demonstrate the DST’s ability to identify mechanism-driven dynamics at a wide range of timescales and its relative insensitivity to functional parameterization. As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms’ utility by using them to extract both a typology of mechanistic local dynamics and a data-driven narrative of socially-important events as perceived by English-language Twitter

Inferring the geolocation of tweets at a fine-grained level

Recently, the use of Twitter data has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency management. These applications require to know the precise location of the tweets for their analysis. However, approximately 1% of the tweets are finely-grained geotagged, which remains insufficient for such applications. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data to support the applications mentioned above. Nevertheless, existing approaches on tweet geolocalisation are mostly focusing on the geolocation of tweets at a coarse-grained level of granularity (i.e., city or country level). Thus, geolocalising tweets at a fine-grained level (i.e., street or building level) has arisen as a newly open research problem. In this thesis, we investigate the problem of inferring the geolocation of non-geotagged tweets at a fine-grained level of granularity (i.e., at most 1 km error distance). In particular, we aim to predict the geolocation where a given tweet was generated using its text as a source of evidence. This thesis states that the geolocalisation of non-geotagged tweets at a fine-grained level can be achieved by exploiting the characteristics of the 1\% of already available individual finely-grained geotagged tweets provided by the Twitter stream. We evaluate the state-of-the-art, derive insights on their issues and propose an evolution of techniques to achieve the geolocalisation of tweets at a fine-grained level. First, we explore the existing approaches in the literature for tweet geolocalisation and derive insights on the problems they exhibit when adapted to work at a fine-grained level. To overcome these problems, we propose a new approach that ranks individual geotagged tweets based on their content similarity to a given non-geotagged. Our experimental results show significant improvements over previous approaches. Next, we explore the predictability of the location of a tweet at a fine-grained level in order to reduce the average error distance of the predictions. We postulate that to obtain a fine-grained prediction a correlation between similarity and geographical distance should exist, and define the boundaries were fine-grained predictions can be achieved. To do that, we incorporate a majority voting algorithm to the ranking approach that assesses if such correlation exists by exploiting the geographical evidence encoded within the Top-N most similar geotagged tweets in the ranking. We report experimental results and demonstrate that by considering this geographical evidence, we can reduce the average error distance, but with a cost in coverage (the number of tweets for which our approach can find a fine-grained geolocation). Furthermore, we investigate whether the quality of the ranking of the Top-N geotagged tweets affects the effectiveness of fine-grained geolocalisation, and propose a new approach to improve the ranking. To this end, we adopt a learning to rank approach that re-ranks geotagged tweets based on their geographical proximity to a given non-geotagged tweet. We test different learning to rank algorithms and propose multiple features to model fine-grained geolocalisation. Moreover, we investigate the best performing combination of features for fine-grained geolocalisation. This thesis also demonstrates the applicability and generalisation of our fine-grained geolocalisation approaches in a practical scenario related to a traffic incident detection task. We show the effectiveness of using new geolocalised incident-related tweets in detecting the geolocation of real incidents reports, and demonstrate that we can improve the overall performance of the traffic incident detection task by enhancing the already available geotagged tweets with new tweets that were geolocalised using our approach. The key contribution of this thesis is the development of effective approaches for geolocalising tweets at a fine-grained level. The thesis provides insights on the main challenges for achieving the fine-grained geolocalisation derived from exhaustive experiments over a ground truth of geotagged tweets gathered from two different cities. Additionally, we demonstrate its effectiveness in a traffic incident detection task by geolocalising new incident-related tweets using our fine-grained geolocalisation approaches

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe\u27s ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp

New computational methods and plant models for evolutionary genomics

This thesis is in the service of a greater understanding of the genetic basis of adaptive traits. Chapter 1 introduces background literature relevant to this thesis. Chapters 2, 3, and 4 develop novel methods and software for the analysis of genetic sequencing data. Chapter 5 details a large collaborative project to establish genetic resources in the model cereal Brachypodium, and perform a genome-wide association study for several agriculturally-relevant traits under two climate change scenarios. Chapter 6 investigates the spatial genetic patterns in two species of woodland eucalypt, and determines the landscape process that could be driving these patterns. Finally, Chapter 7 summarises these works, and proposes some areas of further study. In Chapters 2 and 3, I develop methods that enable analysis of Genotyping-by-sequencing analysis. Axe, a short read sequence demultiplexer, demultiplexes samples from multiplexed GBS sequencing datasets. I show Axe has high accuracy, and outperforms previously published software. Axe also tolerates complex indexing schemes such as the variable-length combinatorial indexes used in GBS data. Trimit and libqcpp (Chapter 3) implements several low-level sequence read quality assessment and control methods as a C++ library, and as a command line tool. Both these works have been published in peer-reviewed journals, and are used by numerous groups internationally. In Chapter 4, I develop kWIP, a de novo estimator of genetic distance. kWIP enables rapid estimation of genetic distances directly from sequence reads. We first show kWIP outperforms a competing method at low coverage using simulations that mimic a population resequencing experiment. We propose and demonstrate several use cases for kWIP, including population resequencing, initial assessment of sample identity, and estimating metagenomic similarity. kWIP was published in PLoS Computational Biology. In Chapter 5, I present the results of a large, collaborative project which surveys the global genetic diversity of the model cereal Brachypodium. We amass a collection of over 2000 accessions from the Brachypodium species complex. Using GBS and whole genome sequencing we identify around 800 accessions of the diploid Brachypodium distachyon, within which we find extensive population structure and clonal families. Through population restructuring we create a core collection of 74 accessions containing the majority of genetic diversity in the "A genome" sub-population. Using this core collection, we assay several phenotypes of agricultural interest including early vigour, harvest index and energy use efficiency under two climates, and dissect the genetic basis of these traits using a genome-wide association study (GWAS). This work has been accepted for publication at Genetics; I am co-first author with Pip Wilson and Jared Streich, having lead many genomic analyses. In Chapter 6, I perform a study of landscape genomic variation in two woodland eucalypt species. Using whole genome sequencing of around 200 individuals from around 20 localities of both E. albens and E. sideroxylon, I find incredible genetic diversity and low genome-wide inter-species differentiation.I find no support for strong discrete population structure, but strong support for isolation by (geographic) distance (IBD). Using generalised dissimilarity modelling, I further examine the pattern of IBD, and establish additional isolation by environment (IBE). E. albens shows moderately strong IBD, explaining 26% of deviance in genetic distance using geographic distance, and an additional 6% deviance explained by incorporating environmental predictors (IBE). E. sideroxylon shows much stronger IBD, with 78% of deviance explained by geography, and stronger IBE (12% additional deviance explained). This work will soon be submitted for publication

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility