Report on requirement analysis and design of PAUSANIAS

Nowadays an increasing amount of web-accessible information on spatial objects becomes available to the public every day. Apart from the spatial location of an object (e.g., a point of interest), additional descriptive information typically includes textual description as well as various ratings, often user generated. Modern applications employ spatio-textual queries, which take into account both the spatial location of an object and its textual similarity to retrieve the most relevant objects. However, existing applications provide a limited functionality to the users. For example, several meaningful queries cannot be expressed by existing approaches and motivate our novel prototype system. The goal of our research is to introduce a novel framework, called Pausanias, for supporting ranked spatial-keyword search over web-accessible geotagged data. In this deliverable, we provide an analysis of requirements that should be fulfilled by the proposed prototype and a concise description of the system design of Pausanias

Summary characteristics for multivariate function-valued spatial point process attributes

Prompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function-valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging instance of such modern spatial data applications. Indeed, the analysis of spatial random point configurations, where the point attributes themselves are functions rather than scalar-valued quantities, is just in its infancy, and extensions to function-valued quantities still remain limited. In this view, we extend current existing first- and second-order summary characteristics for real-valued point attributes to the case where in addition to every spatial point location a set of distinct function-valued quantities are available. Providing a flexible treatment of more complex point process scenarios, we build a framework to consider points with multivariate function-valued marks, and develop sets of different cross-function (cross-type and also multi-function cross-type) versions of summary characteristics that allow for the analysis of highly demanding modern spatial point process scenarios. We consider estimators of the theoretical tools and analyse their behaviour through a simulation study and two real data applications.Comment: submitted for publicatio

Efficient Retrieval of Top-k Weighted Spatial Triangles

Due to the proliferation of location-based services and IoT devices, a lot of spatial points are being generated. Spatial data analysis is well known to be an important task. As spatial data analysis tools, graphs consisting of spatial points, where each point has edges to its nearby points and the weight of each edge is the distance between the corresponding points, have been receiving much attention. We focus on triangles (one of the simplest sub-graph patterns) in such graphs and address the problem of retrieving the top-k weighted spatial triangles. This problem has important real-life applications, e.g., group search, urban planning, and co-location pattern mining. However, this problem is computationally challenging, because the number of triangles in a graph is generally huge and enumerating all of them is not feasible. To solve this challenge, we propose an efficient algorithm that returns the exact result. Our experimental results on real datasets show the efficiency of our algorithm.This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-00123-9_17

Spatial Interaction Models in a Big Data Grocery Retailing Environment

Grocery expenditure is responsible for around 10% of total household spend in the UK, making the grocery retail market worth over £200bn a year in 2021. The size of this market and the nature of retailing competition makes it important for retailers to make the right decisions. One such decision is the location of their stores for which there have been a number of changes in the location, format and channel of consumer interaction along with the methods that have been employed to determine new store location. In recent years it has been suggested that the spatial interaction model is the most appropriate method for estimating new store revenue and hence location. However, previous attempts to explore the performance of the spatial interaction model in grocery retailing have been limited by access to loyalty card data. In this thesis we show that these models are unable to account for the heterogeneity in store conditions and consumer behaviour to model total store revenue. Notably, we find that at the regional scale the size of the errors are such that these models are unlikely to be used consistently in practice for estimating store revenue or locating new stores. Furthermore, that the performance achieved in previous applications are unlikely to be consistently replicated. Thus our results demonstrate that the spatial interaction model in its current form is no longer appropriate for modelling grocery store revenue. It is anticipated that these results may become a starting point for the development and application of alternative forms of models and methods for predicting grocery retailing store revenue. Notably, such new methods must be able to account for recent changes in consumer behaviour such as convenience store shopping, multi-purpose trips and the growing influence of e-commerce, alongside changes in retailers interaction strategies

Outage Capacity of Opportunistic Beamforming with Random User Locations

This paper studies the outage capacity of a network consisting of a multitude of heterogenous mobile users, and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogenous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. It is shown that the beam outage capacity scales logarithmically for the unbounded model. On the other hand, this scaling behavior becomes double logarithmic for the bounded model. Intuitive explanations are provided as to why we observe different scaling behavior for different path loss models. Numerical evaluations are performed to give further insights, and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius.Comment: To appear in Globecom 2013, Atlanta, US

Exploratory methods for the study of incomplete and intersecting shape boundaries from landmark data

Structured spatial point patterns appear in many applications within the natural sciences. Often the points record the location of key features, called landmarks, on continuous object boundaries, such as anatomical features on a human face or on an animal skull. In other situations, the points may simply be arbitrarily spaced marks along a smooth curve, such as on handwritten numbers or letters. Sometimes the points may record the location of clearly visible features from a general structure which has disappeared, such as building foundations at an archaeological site. This paper proposes novel exploratory methods for the identification of structure within point datasets. In particular, points are linked together to form curves which estimate the original shape from which the points are the only recorded information. Nonparametric regression methods are applied to polar coordinate variables obtained from the point locations and periodic modelling allows closed curves to be fitted to circular and elliptical shapes even when data are available on only part of the boundary. Further, the model allows discontinuities to be identified to describe rapid changes in the curves. These generalizations are particularly important when the points represent shapes which are occluded or are intersecting. A range of real-data examples is used to motivate the modelling and to illustrate the flexibility of the approach. The method successfully identifies underlying structure and its output could also be used as the basis for further analysis