Data Mining - Concepts and Techniques.

Statistics for Spatial Data.

Transactions of the Institute of British Geographers

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Data Nodes, Edges Display Interactive Display Visual Analogues VisualItems in ItemRegistry User Figure 6.3: The Information Visualisation Reference Model, adapted from Heer et al.[57] 6.2 State of the Art 93 a visual analytics issue that should be better tackled by all the visualisation communities. Blending different kinds of visualisations in the same application is becoming Blending different kinds of visualisations is currently difficult more frequent. Scientific visualisation and geographic visualisation need information visualisation because they manage multi-valued data with complex topologies that can be visualised using their canonical geometry. In addition, they can also be explored with more abstract visual representations to avoid geometric artefacts. For example, census data can be visualised as a coloured map but also as a multi-dimensional dataset where the longitude and latitude are two attributes among others. Clustering this data by some similarity measure will then reveal places that can be far away in space but behave similarly in term of other attributes (e.g., level of education, level of income, size of houses etc.), similarity that would not be visible on a map. On top of these visualisation systems, a user interface allows control of the overall application. User interfaces are well understood but they can be very different in styles. 3D systems use specific types of interfaces that are very different to traditional desktop interfaces. Moreover, information visualisation systems tend to deeply embed the interaction with the visualisation, offering special kinds of controls either directly inside the visualisations (e.g., range sliders on the axes of parallel coordinates) or around it but with special kinds of widgets (e.g., range sliders for performing range-queries). Interoperability can thus be described at several levels. At the data management level, at the architecture model level and at the interface level. 6.2.2 Data Management All visual analytics applications start with data that can be either statically collected or dynamically produced. Depending on the nature of the data, visual analytics applications have used various ways of managing their storage. In order of sophistication, they are: Flat files using ad-hoc formats, Structured file formats such as XML, Specialised NoSQL systems, including Cloud Storage, Standard or extended transactional databases (SQL), Workflow or dataflow systems integrating storage, distribution and data processing. We will now consider these data storage methods, paying particular attention to Data Management for visual analytics can rely on different levels of sophistication the levels of service required by visual analytics, such as: Persistence (they all provide it by definition), Typing, Distribution, Atomic transactions, Notification, Interactive performance, Computation.

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Mastering the information age : solving problems with visual analytics

The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial and temporal dimensions via clustering, sorting and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 contest data set, which contains time-varying, geographically referenced and multivariate data for technology companies in the US

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A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)

Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This research proposes and evaluates a family of six hierarchical regionalization methods. The six methods are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), and the complete linkage (CLK), each of which is constrained with spatial contiguity in two different ways (i.e. the first-order constraining and the full-order constraining). It is discovered that both the Full-Order-CLK and the Full-Order-ALK methods significantly outperform existing methods across four quality evaluations: the total heterogeneity, region size balance, internal variation, and the preservation of data distribution. Moreover, the proposed algorithms are efficient and can find the solution in O(n 2log n) time. With such data scalability, for the first time it is possible to effectively regionalize large data sets that have 10 000 or more spatial objects. A detailed comparison and evaluation of the six methods are carried out with the 2004 US presidential election data.

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

Spatial data mining and geographic knowledge discovery—An introduction

Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.

Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity w...

Diansheng Guo

Papers

A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

Spatial data mining and geographic knowledge discovery—An introduction

Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods