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

The interaction of process and domain in prefrontal cortex during inductive reasoning

AbstractInductive reasoning is an everyday process that allows us to make sense of the world by creating rules from a series of instances. Consistent with accounts of process-based fractionations of the prefrontal cortex (PFC) along the left–right axis, inductive reasoning has been reliably localized to left PFC. However, these results may be confounded by the task domain, which is typically verbal. Indeed, some studies show that right PFC activation is seen with spatial tasks. This study used fMRI to examine the effects of process and domain on the brain regions recruited during a novel pattern discovery task. Twenty healthy young adult participants were asked to discover the rule underlying the presentation of a series of letters in varied spatial locations. The rules were either verbal (pertaining to a single semantic category) or spatial (geometric figures). Bilateral ventrolateral PFC activations were seen for the spatial domain, while the verbal domain showed only left ventrolateral PFC. A conjunction analysis revealed that the two domains recruited a common region of left ventrolateral PFC. The data support a central role of left PFC in inductive reasoning. Importantly, they also suggest that both process and domain shape the localization of reasoning in the brain

A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance

A detection theory account of change detection

Previous studies have suggested that visual short-term memory (VSTM) has a storage limit of approximately four items. However, the type of high-threshold (HT) model used to derive this estimate is based on a number of assumptions that have been criticized in other experimental paradigms (e.g., visual search). Here we report findings from nine experiments in which VSTM for color, spatial frequency, and orientation was modeled using a signal detection theory (SDT) approach. In Experiments 1-6, two arrays composed of multiple stimulus elements were presented for 100 ms with a 1500 ms ISI. Observers were asked to report in a yes/no fashion whether there was any difference between the first and second arrays, and to rate their confidence in their response on a 1-4 scale. In Experiments 1-3, only one stimulus element difference could occur (T = 1) while set size was varied. In Experiments 4-6, set size was fixed while the number of stimuli that might change was varied (T = 1, 2, 3, and 4). Three general models were tested against the receiver operating characteristics generated by the six experiments. In addition to the HT model, two SDT models were tried: one assuming summation of signals prior to a decision, the other using a max rule. In Experiments 7-9, observers were asked to directly report the relevant feature attribute of a stimulus presented 1500 ms previously, from an array of varying set size. Overall, the results suggest that observers encode stimuli independently and in parallel, and that performance is limited by internal noise, which is a function of set size

A hybrid model for capturing implicit spatial knowledge

This paper proposes a machine learning-based approach for capturing rules embedded in users’ movement paths while navigating in Virtual Environments (VEs). It is argued that this methodology and the set of navigational rules which it provides should be regarded as a starting point for designing adaptive VEs able to provide navigation support. This is a major contribution of this work, given that the up-to-date adaptivity for navigable VEs has been primarily delivered through the manipulation of navigational cues with little reference to the user model of navigation