A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

An Approach for spatial and temporal data analysis: application for mobility modeling of workers in Luxembourg and its bordering areas

In this paper, we propose a general visual analytic approach to synthesis very large spatial data and discover interesting knowledge and unknown patterns from complex data based on Origin-Destination (OD) matrices. The research studies of Tobler constitute a good basis in this topic. This paper is interested in the proposal of 2 methods entitled respectively ?Weighted Linear Directional Mean: WLDM? and ?DS-WLDM?. The latter incorporates the Dempster-Shafer theory of evidence with WLDM. Both of the developed methods are an extension of ?Linear Directional Mean: LDM? for mobility modeling. With classical techniques such as LDM among others, the results of data mapping are not intelligible and easy to interpret. However with both WLDM and DS-WLDM methods it is easy to discover knowledge without losing a lot of information which is one of the interests of this paper. This proposal is generic and it intends to be applied for data mapping such as for geographical presentation of social and demographic information (e.g. mobility of people, goods and information) according to multiple spatial scales (e.g. locality, district, municipality). It could be applied also in transportation field (e.g. traffic flow). For the application, administrative data is used in order to evaluate spatial and temporal aspects of the daily and the residential mobility of workers in Luxembourg and its bordering areas.Mobility modeling; data mapping; spatial mobility; geographic knowledge discovery; location uncertainty; daily and residential mobility

Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data

We live in a data-rich environment where massive amounts of data such as text messages, articles, images, and search queries are continuously generated by users. In this environment, new opportunities to discover and utilize knowledge about the real-world arise, such as the extraction and description of places and events from social media records, the organization of documents by spatio-temporal topics, and the prediction of epidemics by search engine queries. Major challenges addressed in these data- and application-specific works arise from the unstructured and complex nature of the data, and the high level of uncertainty and sparsity of the attributes. Despite the evident progress in utilizing specific data sources for different applications, there remains a lack of common concepts and techniques on how to exploit the data as high-quality sensors of geographic space in a general manner. However, such a general point of view allows to address the common challenges and to define fundamental building blocks to deal with problems in fields like information retrieval, recommender systems, market research, health surveillance, and social sciences. In this thesis, we develop concepts and techniques to utilize various kinds of user-generated data as a steady source of information about geographic processes and entities (together called geographic phenomena). For this, we introduce a novel conceptual data mining framework, called geographic feature mining, that provides the foundation to discover and extract highly informative and discriminative dimensions of geographic space in a unifying and systematic fashion. This is achieved by representing the qualitative and geographic information in the records as geographic feature signals, each constituting a potential dimensions to describe geographic space. The mining process then determines highly informative features or feature combinations from the candidate sets that can be used as a steady source of auxiliary information for domain-specific applications. In developing the framework, we make contributions to several fundamental problems: (1) We introduce a novel probabilistic model to extract high-quality geographic feature signals. The signals are robust to noise and background distributions, and the model allows to exploit diverse kinds of qualitative and geographic information in the records. This flexibility is achieved by utilizing a Bayesian network model and the robustness by choosing appropriate prior distributions. (2) We address the problem of categorizing and selecting geographic features based on their spatio-temporal type, such as feature signals having landmark, regional, or global semantics. For this, we introduce representations of the signals by interaction characteristics and evaluate their performance in clustering and data summarization tasks. (3) To extract a small number of highly informative feature combinations that reflect geographic phenomena, we introduce a model that extracts latent geographic features from the candidate signals using dimensionality reduction. We show that this model outperforms document-centric topic models with respect to the informativeness of the extracted phenomena, and we exhaustively evaluate how different statistical properties of the approaches affect the characteristics of the resulting feature combinations

Geographic knowledge discovery from sparse GPS-data : Revealing spatio-temporal patterns of Amazonian river transports

A vast amount of spatio-temporal data has become available with the fast development of information technology and different monitoring systems over the last two decades. Position-aware devices are one of the most dominant sources for collecting movement data. Spatio-temporal information that is derived from the tracking devices enable to build movement patterns from the targets, and to calculate measurable motion parameters such as speed, change of speed or the direction of movement. This study utilized a specific pilot GPS-based monitoring system called Amazonian Riverboat Observation System (AROS) that was built to collect movement data of the local riverboats on the departments of Loreto and Ucayali in Peruvian Amazonia. AROS provides real-time GPS-data with coordinates and timestamp that indicate where and when the collaborating vessels are navigating. As an outcome of this thesis a specific analytical tool called Trajectory Reconstruction and Analysis Tool (TRAT) was developed. TRAT utilizes variety of geographic knowledge discovery methods to extract knowledge from movement data provided by AROS. Also spatio-temporal transportation characteristics in the study area were analyzed based on AROS data from the year 2012 and utilizing TRAT. This thesis focused on studying if there is seasonal and directional variation in transportation characteristics along the Amazonian rivers, and if river morphology affects the navigation. Also connection between water height of the rivers and travel speed of individual journeys was studied. Results of the thesis suggest that navigation along the rivers has seasonal and directional variation, and also the river morphology seems to affect the movement patterns of the vessels. On navigation route that was mostly meandering by river morphology, the downstream navigation was over 40% faster than upstream navigation during high water and intermediate, but during low water there was no difference between navigation directions. Seasonal variation was over 30% faster during high water compared to low water (on downstream direction). On upstream direction the navigation was fastest during low water but seasonal differences were considerably lower compared to downstream navigation. On navigation route that was mostly anastomosing by river morphology, the downstream navigation was approximately 20 % faster during the entire year. Results suggest that there is no seasonal difference in navigation characteristics along the larger and wider rivers, since the travel speeds were quite similar throughout the year. Fitting simple regression model between average travel speed of the journeys and water levels of the river revealed that there seems to be strong connection between travel speed and river height on the route along Ucayali river when travelled downstream (R2=0.73). On other cases that were studied, the results suggest that there is not connection between travel speed characteristics and river height. Comparing the results with earlier studies implied that the results of this thesis seemed to be fairly accurate. However, it is necessary to validate the results by doing cross-validations between data from different years observed with AROS. Transportation is in a key role when trying to find the factors affecting on development of a certain location. Thus transportation as means of accessibility has significant role in variety of contexts such as conversation, land use changes and deforestation. Results of this study could provide more accurate data for studies focusing on previously mentioned topics in the study area. Also utilization of TRAT in other contexts, such as studying global transportation patterns of professional vessels, could be possible by making few modifications to the tool.Informaatioteknologian ja erilaisten seurantajärjestelmien nopea kehitys viimeisten kahden vuosikymmenen aikana on mahdollistanut massiivisten spatio-temporaalisten tietovarantojen keräämisen. Paikannusteknologioilla varustetut laitteet ovat keskeisimpiä datalähteitä spatio-temporaalisen liikkumistiedon keräämiseen, ja tällainen data mahdollistaa erilaisten kohteiden (liikennevälineet, ihmiset jne.) liikkumisrakenteiden tutkimisen sekä erilaisten liikkumisparametrien kuten nopeuden, ja nopeuden sekä kulkusuunnan muutoksen laskemisen. Tässä tutkimuksessa hyödynnetään eristyistä pilotti-seurantajärjestelmää (AROS), joka on kehitetty keräämään jokilaivojen liikkumisdataa Loreton ja Ucayalin seuduilla Perun Amazoniassa. AROS mahdollistaa reaaliaikaisten laivojen sijantitietojen (koordinaatit) sekä aikatiedon (aikaleima) keräämisen. Tässä tutkimuksessa kehitettiin erityinen liikkumistiedonlouhintaan tarkoitettu analyysityökalu (TRAT), joka hyödyntää useita spatiaalisen tiedonlouhinnan menetelmiä informaation louhimiseksi AROS datasta. Tutkimuksessa tutkittiin, onko AROS datan perusteella jokinavigoinnissa nähtävissä vuodenaikaista vaihtelua vuoden 2012 aikana, ja vaikuttaako kulkusuunta sekä jokimorfologia navigointinopeuksiin. Tutkimuksessa tutkittiin myös, onko jokien vedenkorkeuksilla yhteyttä navigointinopeuksiin. Tutkimuksen tulokset osoittivat, että navigointi vaihtelee riippuen vuodenajasta sekä kulkusuunnasta, ja myös viitteitä jokimorfologian vaikutuksesta navigointiin oli paikoittain nähtävissä. Meanderoivilla jokiosuuksilla navigoiminen alavirtaan oli n. 40 % nopeampaa korkeanveden aikaan, mutta matalanveden aikaan eroa nopeuksissa ei ollut juuri nähtävissä. Vuodenaikaisvaihtelu oli selkeintä alavirtaan kuljettaessa, jolloin navigointi korkeanveden aikaan oli n. 30 % nopeampaa verrattuna matalanveden aikaan. Anastomoivilla jokiosuuksilla erot nopeuksissa eri kulkusuuntiin olivat vähäisemmät, ja navigointi oli keskimäärin 20 % nopeampaa alavirtaan (verrattuna ylävirtaan). Vuodenaikaisvaihtelua ei ollut juurikaan nähtävissä. Lineaarien regressiomalli jokikorkeuksien ja yksittäisten osareittien navigointinopeuksien välille osoitti, että yhteys oli selkeä (R2=0.73) osareiteillä, jotka kulkivat Ucayali-jokea alavirtaan. Muissa tutkituissa tapauksissa selkää yhteyttä ei löytynyt. Vertailemalla työn tuloksia aiempiin tutkimuksiin osoitti, että tulokset vaikuttavat olevan linjassa muiden tutkimusten tulosten kanssa. Työn tuloksia tulee jatkossa tosin vielä validoida vertailemalla vuoden 2012 tuloksia muiden vuosien tuloksiin AROS datan perusteella. Liikennejärjestelmät ovat keskeisiä tekijöitä, jotka vaikuttavat alueiden yleiseen kehitykseen. Yksi tapa kuvata liikennerakenteita on tarkastella paikkojen välistä saavutettavuutta, jolla on todettu olevan merkitystä lukuisiin eri yhteyksissä kuten maankäytön muutoksessa, deforestaatiossa sekä luonnonsuojelussa. Tämän tutkimuksen tulokset voivat tarjota tarkempaa dataa ja informaatiota liittyen edellämainittujen aiheiden tutkimiseen Perun Amazoniassa ja mahdolllisesti muillakin Amazonin alueilla. Kehitettyä analyysityökalua (TRAT) on myös mahdollista hyödyntää laajemmissa yhteyksissä, kuten globaalin laivaliikenteen tutkimuksessa, tekemällä pieniä muutoksia työkalun algoritmeihin

An unsupervised approach to Geographical Knowledge Discovery using street level and street network images

Recent researches have shown the increasing use of machine learn-ing methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data using a supervised approach. Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery. In particular, we propose a simple approach called Convolutional-PCA(ConvPCA) which are applied on both street level and street network images to find a set of uncorrelated and ordered visual latentcomponents. The approach allows for meaningful explanations using a combination of geographical and generative visualisations to explore the latent space, and to show how the learned representation can be used to predict urban characteristics such as streetquality and street network attributes. The research also finds that the visual components from the ConvPCA model achieves similaraccuracy when compared to less interpretable dimension reduction techniques.Comment: SigSpatial 2019 GeoA