Increasing penetration of stochastic photovoltaic (PV) generation into the electric power system poses significant challenges to system operators. In the thesis, we evaluate the spatial and temporal correlations of stochastic PV generation at multiple sites. Given the unique spatial and temporal correlation of PV generation, an optimal data-driven forecast model for short-term PV power is proposed. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared with conventional persistent model. The tradeoff between communication cost and improved forecast quality is studied using realistic data sets collected from California and Colorado. n IEEE 14 bus system test case is used to quantify the value of improved forecast quality through the reduction of system dispatch cost. The Modified spatio-temporal forecast model which has the least forecast PV overestimate percentage shows the best performance in the dispatch cost reduction.

This dissertation challenges an unstudied area in moving objects database domains; predicting (long-term) future locations of moving objects. Moving object prediction enables us to provide a wide range of applications, such as traffic prediction, pre-detection of an aircraft collision, and reporting attractive gas prices for drivers along their routes ahead. Nevertheless, existing location prediction techniques are limited to support such applications since they are generally capable only of short-term predictions. In the real world, many objects exhibit typical movement patterns. This pattern information is able to serve as an important background to tackle the limitations of the existing prediction methods. We aims at offering foundations of pattern-aware prediction for moving objects, rendering more precise prediction results. Specifically, this thesis focuses on three parts. The first part of the thesis studies the problem of predicting future locations of moving objects in Euclidean space. We introduce a novel prediction approach, termed the hybrid prediction model, which utilizes not only the current motion of an object, but also the object's trajectory patterns for prediction. We define, mine, and index the trajectory patterns with a novel access method for efficient query processing. We then propose two different query processing techniques along given query time, i.e., for near future and for distant future. The second part covers the prediction problem for moving objects in network space. We formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical objects trajectories. This model captures turning patterns of the objects at junctions, at the granularity of individual objects as well as globally. Based on the model, we develop three different algorithms for predicting the future path of a mobile user moving in a road network, named the PathPredictors. The third part of the thesis extends the prediction problem for a single object to that for multiple objects. We introduce a convoy query that retrieves all groups of objects, i.e., convoys, from the objects' historical trajectories, each convoy consists of objects that have traveled together for some time; thus they may also move together in the future. We then propose three efficient algorithms for the convoy discovery, called the CuTS family, that adopt line simplification methods for reducing the size of the trajectories, permitting efficient query processing. For each part, we demonstrate comprehensive experimental results of our proposals, which show significantly improved accuracies for moving object prediction compared with state-of-the-art methods, while also facilitating efficient query processing.

This dissertation challenges an unstudied area in moving objects database domains; predicting (long-term) future locations of moving objects. Moving object prediction enables us to provide a wide range of applications, such as traffic prediction, pre-detection of an aircraft collision, and reporting attractive gas prices for drivers along their routes ahead. Nevertheless, existing location prediction techniques are limited to support such applications since they are generally capable only of short-term predictions. In the real world, many objects exhibit typical movement patterns. This pattern information is able to serve as an important background to tackle the limitations of the existing prediction methods. We aims at offering foundations of pattern-aware prediction for moving objects, rendering more precise prediction results. Specifically, this thesis focuses on three parts. The first part of the thesis studies the problem of predicting future locations of moving objects in Euclidean space. We introduce a novel prediction approach, termed the hybrid prediction model, which utilizes not only the current motion of an object, but also the object's trajectory patterns for prediction. We define, mine, and index the trajectory patterns with a novel access method for efficient query processing. We then propose two different query processing techniques along given query time, i.e., for near future and for distant future. The second part covers the prediction problem for moving objects in network space. We formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical objects trajectories. This model captures turning patterns of the objects at junctions, at the granularity of individual objects as well as globally. Based on the model, we develop three different algorithms for predicting the future path of a mobile user moving in a road network, named the PathPredictors. The third part of the thesis extends the prediction problem for a single object to that for multiple objects. We introduce a convoy query that retrieves all groups of objects, i.e., convoys, from the objects' historical trajectories, each convoy consists of objects that have traveled together for some time; thus they may also move together in the future. We then propose three efficient algorithms for the convoy discovery, called the CuTS family, that adopt line simplification methods for reducing the size of the trajectories, permitting efficient query processing. For each part, we demonstrate comprehensive experimental results of our proposals, which show significantly improved accuracies for moving object prediction compared with state-of-the-art methods, while also facilitating efficient query processing.

Doctor of Philosophy (PhD) / Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.

In this thesis I will be concerned with linking the observed speechsignal to the configuration of articulators.Due to the potentially rapid motion of the articulators, the speechsignal can be highly non-stationary. The typical linear analysistechniques that assume quasi-stationarity may not have sufficienttime-frequency resolution to determine the place of articulation.I argue that the traditional low and high-level primitives of speechprocessing, frequency and phonemes, are inadequate and should bereplaced by a representation with three layers: 1. short pitch periodresonances and other spatio-temporal patterns 2. articulatorconfiguration trajectories 3. syllables. The patterns indicatearticulator configuration trajectories (how the tongue, jaws, etc. aremoving), which are interpreted as syllables and words.My patterns are an alternative to frequency. I use shorttime-domain features of the sound waveform, which can be extractedfrom each vowel pitch period pattern, to identify the positions of thearticulators with high reliability. These features are importantbecause by capitalizing on detailed measurements within a single pitchperiod, the rapid articulator movements can be tracked. No linearsignal processing approach can achieve the combination of sensitivityto short term changes and measurement accuracy resulting from thesenonlinear techniques.The measurements I use are neurophysiologically plausible: theauditory system could be using similar methods.I have demonstrated this approach by constructing a robust techniquefor categorizing the English voiced stops as the consonants B, D, or Gbased on the vocalic portions of their releases. The classificationrecognizes 93.5%, 81.8% and 86.1% of the b, d and gto ae transitions with false positive rates 2.9%, 8.7% and2.6% respectively.

Examples of spatio-temporal data that can be represented as sets of points (called dot patterns) are pervasive in many applications, for example when tracking herds of migrating animals, ships in busy shipping channels and crowds of people in everyday life. The use of this type of data extends beyond the standard remit of Geographic Information Science (GISc), as classification and optimisation problems can often be visualised in the same manner. A common task within these fields is the assignment of a region (called a footprint) that is representative of the underlying pattern. The ways in which this footprint can be generated has been the subject of much research with many algorithms having been produced. Much of this research has focused on the dot patterns and footprints as static entities, however for many of the applications the data is prone to change. This thesis proposes that the footprint need not necessarily be updated each time the dot pattern changes; that the footprint can remain an appropriate representation of the pattern if the amount of change is slight. To ascertain the appropriate times at which to update the footprint, and when to leave it as it is, this thesis introduces the concept of change identifiers as simple measures of change between two dot patterns. Underlying the change identifiers is an in-depth examination of the data inherent in the dot pattern and the creation of descriptors that represent this data. The experimentation performed by this thesis shows that change identifiers are able to distinguish between different types of change across dot patterns from different sources. In doing so the change identifiers reduce the number of updates of the footprint while maintaining a measurably good representation of the dot pattern.

The environmental risk of growing genetically modified (GM) crops and particularly the spreading of GM genes to related non-GM crops is currently a concern in European agriculture. Because the risks of contamination are linked to the spatial and temporal arrangements of crops within the landscape, scenarios of crop arrangement are required to investigate the risks and potential coexistence measures. However, until recently, only manual methods were available to create scenarios. This thesis aims to provide a flexible referenced tool to create such scenarios. The model, called LandSFACTS, is a scientific research tool which allocates crops into fields, to meet user-defined crop spatio-temporal arrangements, using an empirical and statistical approach. The control of the crop arrangements is divided into two main sections: (i) the temporal arrangement of crops: encompassing crop rotations as transition matrices (specifically-developed methodology), temporal constraints (return period of crops, forbidden crop sequences), initial crops in fields regulated by temporal patterns (specifically-developed statistical analyses) and yearly crop proportions; and (ii) the spatial arrangements of crops: encompassing possible crops in fields, crop rotation in fields regulated by spatial patterns (specifically-developed statistical analyses), and spatial constraints (separation distances between crops). The limitations imposed by the model include the size of the smallest spatial and temporal unit: only one crop is allocated per field and per year. The model has been designed to be used by researchers with agronomic knowledge of the landscape. An assessment of the model did not lead to the detection of any significant flaws and therefore the model is considered valid for the stated specifications. Following this evaluation, the model is being used to fill incomplete datasets, build up and compare scenarios of crop allocations. Within the GM coexistence context, the model could provide useful support to investigate the impact of crop arrangement and potential coexistence measures on the risk of GM contamination of crops. More informed advice could therefore be provided to decision makers on the feasibility and efficiency of coexistence measures for GM cultivation.

Doctor of Philosophy / Department of Statistics / Juan Du / It is common to assume the spatial or spatio-temporal data are realizations of underlying random elds or stochastic processes. E ective approaches to modelling of the underlying autocorrelation structure of the same random eld and the association among multiple processes are of great demand in many areas including atmospheric sciences, meteorology and agriculture. To this end, this dissertation studies methods and application of the spatial modeling of large-scale dependence structure and spatio-temporal regression modelling. First, variogram and variogram matrix functions play important roles in modeling dependence structure among processes at di erent locations in spatial statistics. With more and more data collected on a global scale in environmental science, geophysics, and related elds, we focus on the characterizations of the variogram models on spheres of all dimensions for both stationary and intrinsic stationary, univariate and multivariate random elds. Some e cient approaches are proposed to construct a variety of variograms including simple polynomial structures. In particular, the series representation and spherical behavior of intrinsic stationary random elds are explored in both theoretical and simulation study. The applications of the proposed model and related theoretical results are demonstrated using simulation and real data analysis. Second, knowledge of the influential factors on the number of days suitable for fieldwork (DSFW) has important implications on timing of agricultural eld operations, machinery decision, and risk management. To assess how some global climate phenomena such as El Nino Southern Oscillation (ENSO) a ects DSFW and capture their complex associations in space and time, we propose various spatio-temporal dynamic models under hierarchical Bayesian framework. The Integrated Nested Laplace Approximation (INLA) is used and adapted to reduce the computational burden experienced when a large number of geo-locations and time points is considered in the data set. A comparison study between dynamics models with INLA viewing spatial domain as discrete and continuous is conducted and their pros and cons are evaluated based on multiple criteria. Finally a model with time- varying coefficients is shown to reflect the dynamic nature of the impact and lagged effect of ENSO on DSFW in US with spatio-temporal correlations accounted.

The increase in wind energy production has been relatively rapid and is expected to continue at a global scale. However, numbers of bat carcasses found at wind turbines in North America in the early 21st century raised concern about the plight of this taxon with the growth in wind-energy generation. This led to carcass searches for bats becoming commonplace at wind farms globally. However, few large scale systematic studies have assessed the effects of wind turbines on bats, especially for species considered potentially at higher risk in Europe. In this thesis the number and species of bats killed from wind farms were estimated across Britain, and the important predictors (i.e. activity, turbine characteristics and habitat) of fatality were determined. Insect abundance, biomass and bat activity was also quantified at turbine and control locations, to assess if insects and hence bats were attracted to turbines. In addition, assessments were made of the effects of increasing temporal and spatial replication of acoustic monitoring on estimates of species composition and bat activity. This was assessed for activity monitored at ground and at the centre of the rotor sweep area (the nacelle). Carcass searches were conducted using trained search dogs and concurrently bats were surveyed acoustically at three randomly selected turbines at ground and from the nacelle at 48 wind farms throughout Britain. Bats were also monitored acoustically at paired controls (with a randomly selected turbine) at 20 of the wind farms sites. In addition, nocturnal Diptera were sampled at 18 of the sites using a paired turbine and control design. Across 139 wind turbines, 188,335 bat passes were recorded and 2,973 carcass searches performed. Edge and open aerial foraging species, in particular Pipistrellus pipistrellus and P. pygmaeus were most at risk of fatality 4 at wind farms in Britain. The number of Pipistrellus pipistrellus killed annually in Britain between mid-July and mid-October was estimated at 2,373 95% CI 513 to 4,233 and the number of P. pygmaeus at 3,082 95% CI 1,270 to 4,894. When compared to population estimates, the number of Pipistrellus pygmaeus killed was 57% higher than the number of P. pipistrellus killed (0.19% of the population versus 0.43%, respectively). This may be due to Pipistrellus pygmaeus flying more often within the rotor sweep area compared to P. pipistrellus. Activity measured at the nacelle, which is generally assumed to be a better predictor of fatalities, was not a significant predictor of the probability of a fatality for all species combined, Pipistrellus pipistrellus, or P. pygmaeus. Pipistrellus pipistrellus activity and P. pygmaeus activity, measured at ground level were not good predictors of their respective fatalities. Whilst there was some evidence that Pipistrellus pipistrellus and P. pygmaeus activity monitored at ground level, was a significant predictor of the probability of their respective fatalities occurring, across wide ranging turbine types, fatality estimates were large. This is presumably due to the importance of turbine characterises (the wind speed that turbines become operational (cut-in speeds) turbine and the distance between the ground and blade tip at the bottom of the rotor sweep area) both being important negative predictors of fatalities for Pipistrellus pipistrellus. Predicting from models, if the cut-in speed is increased from 3.5 to 5 m s-1 the number of Pipistrellus pipistrellus fatalities would be reduced by 76% (0.23 fatalities per turbine per month to 0.06). These findings have important implications for guidance, since activity is the ubiquitous measure used to assess fatality risk for all species. Since, Pipistrellus pipistrellus and P. pygmaeus were detected at 98% and 92% of sites respectively; it could be 5 assumed that these species would be detected at the majority of wind farms within their range. Therefore, in a British context, curtailing wind turbines below 5 m s-1 could be an effective mitigation strategy without the costly requirement to monitor activity. Pipistrellus pipistrellus and P. pygmaeus activity was 46% (6.3 ± 1.3 SE mean passes per night c.f. 3.4 ± 1.3 SE) and 34% (4.0 ± 1.4 SE c.f. 2.7 ± 1.4 SE) higher at turbines compared to controls, respectively. Given that habitat and elevation were consistent between paired turbines and controls and monitoring was conducted on the same nights, higher activity at turbines compared to controls provides evidence that these two species are attracted to wind turbines. Furthermore, since the biomass of nocturnal Diptera, the main insect prey for Pipistrellus spp., was higher at controls compared to turbines, and bat foraging at turbines was not predicted by insect abundance or biomass, attraction is unlikely to be due to insects. Evidence presented here shows that bats are attracted to turbines, and therefore measuring activity at pre-construction sites for environmental impact assessments is unlikely to be effective. In conclusion, these results provide further evidence that common species are killed but generally in relatively low numbers, they also support the view that monitoring activity for assessing fatality risk at wind farms is ineffective. It is imperative that wind energy is developed using an evidence based approach. However, it also important that wind energy continues to contribute to an increasing renewable energy sector. In conclusion, results presented here, support that wind turbines are likely to be having a small impact on bat populations in Britain.

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.