Academic literature on the topic 'Spatial granularities'

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.

The authors present four human behavioral experiments to address the question of intuitive granularities in fundamental spatial relations as they can be found in formal spatial calculi. These calculi focus on invariant characteristics under certain (especially topological) transformations. Of particular interest to this article is the concept of two spatially extended entities overlapping each other. The overlap concept has been extensively treated in Galton's mode of overlap calculus (Galton, 1998). In the first two experiments, the authors used a category construction task to calibrate this calculus against behavioral data and found that participants adopted a very coarse view on the concept of overlap and distinguished only between three general relations: proper part, overlap, and non-overlap. In the following two experiments, the authors changed the instructions to explicitly address the possibility that humans could be swayed to adopt a more detailed level of granularity, that is, the authors encouraged them to create as many meaningful groups as possible. The results show that the three relations identified in the first two experiments (overlap, non-overlap, and proper part) are very robust and a natural level of granularity across all four experiments. However, the results also reveal that contextual factors gain more influence at finer levels of granularity.

Qualitative spatial descriptions characterize essential properties of spatial objects or configurations by relying on relative comparisons rather than measuring. Typically, in qualitative approaches only relatively coarse distinctions between configurations are made. Qualitative spatial knowledge can be used to represent incomplete and underdetermined knowledge in a systematic way. This is especially useful if the task is to describe features of classes of configurations rather than individual configurations. Although reasoning with them is generally NP-hard, relative directions are important because they play a key role in human spatial descriptions and there are several approaches how to represent them using qualitative methods. In these approaches directions between spatial locations can be expressed as constraints over infinite domains, e.g. the Euclidean plane. The theory of relation algebras has been successfully applied to this field. Viewing relation algebras as universal algebras and applying and modifying standard tools from universal algebra in this work, we (re)define notions of qualitative constraint calculus, of homomorphism between calculi, and of quotient of calculi. Based on this method we derive important properties for spatial calculi from corresponding properties of related calculi. From a conceptual point of view these formal mappings between calculi are a means to translate between different granularities.

Determining the optimal granularity, which has often been ignored in the analysis of urban expansion and its landscape pattern, is the core problem in landscape ecology research. Here, we calculate the optimal granularities for differently sized cities in the Huaihe River Basin of China based on scale transformation and area loss evaluation. Accordingly, we construct a landscape index and urban land density function to analyze urban expansion and landscape pattern. The results can be summarized as follows. (1) Within the first scale domain of the landscape indices, the optimal granularities of Zhengzhou, Xuzhou, Yancheng, Xinyang, and Bozhou are 60 m, 50 m, 40 m, 40 m, and 40 m, respectively, which are the optimal units in the study of urban expansion. (2) The urban land density decreases from the urban center to the outskirts, the urban core of each city is more compact than the outskirts, and the land density curve parameter α of Zhengzhou is the largest at 4.693 and its urban core the most compact. (3) There are significant spatial and temporal differences in the urban land densities of differently sized cities. The urban land density functions of different cities are similar before 2000; after that, they are similar to the standard inverse S-shaped function and the land use density curve of large cities is closer to the standard inverse S-shaped function than that of small- and medium-sized cities. (4) Large cities have faster expansion, much larger land density curve parameter c than medium- and small-cities, stronger linkage development with surrounding areas, and a higher degree of urban centralization. Urban expansion compactness was influenced by urban locations and functions except for urban sizes. This study offers a method for identifying the optimal granularities for differently sized cities and also provides information for the decision-making efforts that concern the rapid urbanization in major grain-producing areas of China.

In this article, an approach is presented for the representation and reasoning over qualitative spatial relations. A set-theoretic approach is used for representing the topology of objects and underlying space by retaining connectivity relationships between objects and space components in a structure, denoted, adjacency matrix. Spatial relations are represented by the intersection of components, and spatial reasoning is achieved by the application of general rules for the propagation of the intersection constraints between those components. The representation approach is general and can be adapted for different space resolutions and granularities of relations. The reasoning mechanism is simple and the spatial compositions are achieved in a finite definite number of steps, controlled by the complexity needed in the representation of objects and the granularity of the spatial relations required. The application of the method is presented over geometric structures that takes into account qualitative surface height information. It is also shown how directional relationships can be used in a hybrid approach for richer composition scenarios. The main advantage of this work is that it offers a unified platform for handling different relations in the qualitative space, which is a step toward developing general spatial reasoning engines for large spatial databases.

As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.

The expanding deployment of sensor systems that capture location, time, and multiple thematic variables is increasing the need for exploratory spatio-temporal data analysis tools. Geographic information systems (GIS) and time series analysis tools support exploration of spatial and temporal patterns respectively and independently, but tools for the exploration of both dimensions within a single system are relatively rare. The contribution of this research is a framework for the visualization and exploration of spatial, temporal, and thematic dimensions of sensor-based data. The unit of analysis is an event, a spatio-temporal data type extracted from sensor data. The conceptual framework suggests an approach for design layout that can be flexibly modified to explore spatial and temporal trends, temporal relationships among events, periodic temporal patterns, the timing of irregularly repeating events, event–event relationships in terms of thematic attributes, and event patterns at different spatial and temporal granularities. Flexible assignment of spatial, temporal, and thematic categories to a set of graphical interface elements that can be easily rearranged provides exploratory power as well as a generalizable design layout structure. The framework is illustrated with events extracted from Gulf of Maine Ocean Observing System data but the approach has broad application to other domains and applications in which time, space, and attributes need to be considered in conjunction.

Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow feature engineering. Nevertheless, they have limited performance in learning complex moving patterns. Thus, a Latent Semantic Pulse Sequence-based Deep Neural Network (LSPS-DNN) is proposed in this paper to improve the taxi travel time estimation performance by constructing a latent semantic propagation graph representing the latent path sequence. It first extracts the shallow modal features of trips, such as the time period and spatial location at different granularities. The representation of the pulse propagation graph is then extracted from shallow spatial features using a Pulse Coupled Neural Network (PCNN). Further, the propagation graph is encoded with negative sampling to obtain the embedding of deep propagation features between ODs. Meanwhile, we conduct deep network learning based on the Chengdu and NYC taxi datasets; our experimental evaluation results show it has a better performance compared to traditional feature construction methods.

In molti campi di ricerca, i ricercatori hanno la necessità di memorizzare, gestire e interrogare dati spazio-temporali. Tali dati sono classici dati alfanumerici arricchiti però con una o più componenti temporali, spaziali e spazio-temporali che, con diversi possibili significati, li localizzano nel tempo e/o nello spazio. Ambiti in cui tali dati spazio-temporali devono essere raccolti e gestiti sono, per esempio, la gestione del territorio o delle risorse naturali, l'epidemiologia, l'archeologia e la geografia. Più in dettaglio, per esempio nelle ricerche epidemiologiche, i dati spazio-temporali possono servire a rappresentare diversi aspetti delle malattie e delle loro caratteristiche, quali per esempio la loro origine, espansione ed evoluzione e i fattori di rischio potenzialmente connessi alle malattie e al loro sviluppo. Le componenti spazio-temporali dei dati possono essere considerate come dei "meta-dati" che possono essere sfruttati per introdurre nuovi tipi di analisi sui dati stessi. La gestione di questi "meta-dati" può avvenire all'interno di diversi framework proposti in letteratura. Uno dei concetti proposti a tal fine è quello delle granularità. In letteratura c'è ampio consenso sul concetto di granularità temporale, di cui esistono framework basati su diversi approcci. D'altro canto, non esiste invece un consenso generale sulla definizione di un framework completo, come quello delle granularità temporali, per le granularità spaziali e spazio-temporali. Questa tesi ha lo scopo di riempire questo vuoto proponendo un framework per le granularità spaziali e, basandosi su questo e su quello già presente in letteratura per le granularità temporali, un framework per le granularità spazio-temporali. I framework proposti vogliono essere completi, per questo, oltre alle definizioni dei concetti di granularità spaziale e spazio-temporale, includono anche la definizione di diversi concetti legati alle granularità, quali per esempio le relazioni e le operazioni tra granularità. Le relazioni permettono di conoscere come granularità diverse sono legate tra loro, costruendone anche una gerarchia. Tali informazioni sono poi utili al fine di conoscere se e come è possibile confrontare dati associati e rappresentati con granularità diverse. Le operazioni permettono invece di creare nuove granularità a partire da altre granularità già definite nel sistema, manipolando o selezionando alcune loro componenti. Basandosi su questi framework, l'obiettivo della tesi si sposta poi sul mostrare come le granularità possano essere utilizzate per arricchire basi di dati spazio-temporali già esistenti al fine di una loro migliore e più ricca gestione e interrogazione. A tal fine, proponiamo qui una base di dati per la gestione dei dati riguardanti le granularità temporali, spaziali e spazio-temporali. Nella base di dati proposta possono essere rappresentate tutte le componenti di una granularità come definito nei framework proposti. La base di dati può poi essere utilizzata per estendere una base di dati spazio-temporale esistente aggiungendo alle tuple di quest'ultima delle referenze alle granularità dove quei dati possono essere localizzati nel tempo e/o nel spazio. Per dimostrare come ciò possa essere fatto, nella tesi introduciamo la base di dati sviluppata ed utilizzata dal Servizio Psichiatrico Territoriale (SPT) di Verona. Tale base di dati memorizza le informazioni su tutti i pazienti venuti in contatto con l'SPT negli ultimi 30 anni e tutte le informazioni sui loro contatti con il servizio stesso (per esempio: chiamate telefoniche, visite a domicilio, ricoveri). Parte di tali informazioni hanno una componente spazio-temporale e possono essere quindi analizzate studiandone trend e pattern nel tempo e nello spazio. Nella tesi quindi estendiamo questa base di dati psichiatrica collegandola a quella proposta per la gestione delle granularità. A questo punto i dati psichiatrici possono essere interrogati anche sulla base di vincoli spazio-temporali basati su granularità. L'interrogazione di dati spazio-temporali associati a granularità richiede l'utilizzo di un linguaggio d'interrogazione che includa, oltre a strutture, operatori e funzioni spazio-temporali per la gestione delle componenti spazio-temporali dei dati, anche costrutti per l'utilizzo delle granularità nelle interrogazioni. Quindi, partendo da un linguaggio d'interrogazione spazio-temporale già presente in letteratura, in questa tesi proponiamo anche un linguaggio d'interrogazione che permetta ad un utente di recuperare dati da una base di dati spazio-temporale anche sulla base di vincoli basati su granularità. Il linguaggio viene introdotto fornendone la sintassi e la semantica. Inoltre per mostrare l'effettivo ruolo delle granularità nell'interrogazione di una base di dati clinica, mostreremo diversi esempi di interrogazioni, scritte con il linguaggio d'interrogazione proposto, sulla base di dati psichiatrica dell'SPT di Verona. Tali interrogazioni spazio-temporali basate su granularità possono essere utili ai ricercatori ai fini di analisi epidemiologiche dei dati psichiatrici.
In several research fields, temporal, spatial, and spatio-temporal data have to be managed and queried with several purposes. These data are usually composed by classical data enriched with a temporal and/or a spatial qualification. For instance, in epidemiology spatio-temporal data may represent surveillance data, origins of disease and outbreaks, and risk factors. In order to better exploit the time and spatial dimensions, spatio-temporal data could be managed considering their spatio-temporal dimensions as meta-data useful to retrieve information. One way to manage spatio-temporal dimensions is by using spatio-temporal granularities. This dissertation aims to show how this is possible, in particular for epidemiological spatio-temporal data. For this purpose, in this thesis we propose a framework for the definition of spatio-temporal granularities (i.e., partitions of a spatio-temporal dimension) with the aim to improve the management and querying of spatio-temporal data. The framework includes the theoretical definitions of spatial and spatio-temporal granularities (while for temporal granularities we refer to the framework proposed by Bettini et al.) and all related notions useful for their management, e.g., relationships and operations over granularities. Relationships are useful for relating granularities and then knowing how data associated with different granularities can be compared. Operations allow one to create new granularities from already defined ones, manipulating or selecting their components. We show how granularities can be represented in a database and can be used to enrich an existing spatio-temporal database. For this purpose, we conceptually and logically design a relational database for temporal, spatial, and spatio-temporal granularities. The database stores all data about granularities and their related information we defined in the theoretical framework. This database can be used for enriching other spatio-temporal databases with spatio-temporal granularities. We introduce the spatio-temporal psychiatric case register, developed by the Verona Community-based Psychiatric Service (CPS), for storing and managing information about psychiatric patient, their personal information, and their contacts with the CPS occurred in last 30 years. The case register includes both clinical and statistical information about contacts, that are also temporally and spatially qualified. We show how the case register database can be enriched with spatio-temporal granularities both extending its structure and introducing a spatio-temporal query language dealing with spatio-temporal data and spatio-temporal granularities. Thus, we propose a new spatio-temporal query language, by defining its syntax and semantics, that includes ad-hoc features and constructs for dealing with spatio-temporal granularities. Finally, using the proposed query language, we report several examples of spatio-temporal queries on the psychiatric case register showing the ``usage'' of granularities and their role in spatio-temporal queries useful for epidemiological studies.

In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i.e., a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i.e., ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task.