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Geospatial Data Analysis: A Review of Theory and Methods

B. Types of Spatial Data

It is important to analyze the discreteness or continuity of space on which the variables are measured. This classification of spatial data by type of conception of space and by measured variables is the first step in specifying the appropriate statistical technique / algorithm to use towards solving the given problem. A topology of spatial data based on the conception of space is provided by Manfred et al [20] . The four types of spatial data are:

  • Point pattern data : A data set consisting of point locations, at which events of interest have occurred like disease or incidence of crime etc.
  • Field data / Geostatistical data : The data set consisting of variable which are conceptually continuous and whose observations are sampled at fixed set of points.
  • Area data : The data set consisting of observations from a fixed set of area objects that may form a lattice like remotely sensed images etc.
  • Spatial interaction data : The data set consisting of observations of a pair of point locations or a pair of areas.

II. ANALYSIS OF GEO-SPATIAL DATA

With abundance in data, the data processing and analysis technologies have been driven by data. Analysis of spatial data is both compute intensive and resource intensive. There is a huge amount of literature available under geospatial data and GIS systems. In this study, we have used the following taxonomy to understand the available literature in this area.

A. Taxonomy for analysis

The large volume of geospatial data raises the question of efficient processing architectures or data processing methodologies for acquiring useful knowledge. Hence, in this study, the available literature is studied based on:

1) Spatial data models / infrastructures.
2) Spatial data analytics platforms / data processing frameworks/systems.
3) Algorithms for spatial data.
4) Applications of spatial data.

III. SPATIAL DATA MODELS / INFRASTRUCTURES

The available literature for spatial data model is more concentrated towards improving the spatial query performance and throughput. Wang et al [2] propose a new geospatial data model X3DORGDM (X3D-based Oriented Relation Geospatial Data Model). It aims to meet the requirements of geovisualization. As a data model, X3DORGDM consists of three components: 1) a collection of geographic data types; 2) a collection of operating algorithms; 3) a collection of integration and consistency rules to define the consistent geo-database or change of state or both.

The kernel of X3DORGDM has been extended from CGAL and new classes and algorithms like Filter Search Tree has been added.

X3D-based ORGDM has been implemented based on several open source packages like POSTGRESQL, OpenGIS Simple Features Data Model, Computational Geometry Algorithm Library(CGAL), Geographic Data Abstraction Library(GDAL) and PROJ.
4) The model extends the existing approaches by utilizing the management and computation of the spatial data in a geo-database and using X3D data flow to aggregate hybrid spatial data. The interface of output data in X3D-based ORGDM deals with the request of data services from clients, which includes retrieving, querying, updating and computing spatial data, and the transmitting of X3D/XML data flow from the Data Management layer to the Business Logic layer and Client Services layer. Rich semantics, higher speed in parsing and more comprehensive visualization performance fully enables X3D file format to build the applications of geographic-visualization-based data mining and knowledge discovery.

Lacasta et al [3] propose a process to construct a Linked Data model of geospatial resources that allows semantic searching and browsing. There are some initiatives by the standardization bodies Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO) to standardize the way geospatial information is created, provided and transformed according to the user needs. However there are issues like: geo-service creators have to manually describe and provide annotations for their services. The publicly available geospatial catalogs have to be manually annotated, for a search of spatial data to yield better results. This work proposes a methodology that combines and adapts a set of information retrieval and natural language processing techniques to the geospatial web service context. It also shows how to use these techniques to create an automatic system that can identify, classify and interrelate and facilitate the access to geospatial web services.

Chi-Ren Shyu et al [4] propose a coherent system: GeoIRIS, that allows image analysts to rapidly identify relevant imagery. GeoIRIS ably answers analysts questions such as given a query image, show database satellite images that have similar objects and spatial relationship that are within a certain radius of landmark. Their architecture consists of modules namely: feature extraction(FE), indexing structures(IS), semantic framework(SF), GeoName Server(GS), fusion and ranking(FR), and retrieval visualization(RV). They use Tile based feature extraction and Object based feature extraction for feature extraction. Indexing of continuous valued features based on entropy balance statistical (EBS) k-dimensional (k-D) tree and indexing the binary-valued features is performed with the entropy balanced bitmap (EBB) tree. It also proposes novel approaches for information ranking, semantic modelling and advanced queries.

S.Roy et al [6] discuss the metadata issue related to Spatial Data Infrastructure; and they attempt to propose a three-tired infrastructure towards the enhancement of metadata catalogue services in regards of three aspects.

1) Incorporating of various geographic information metadata elements and provision of necessary support for spatial data infrastructures;
2) Achieving interoperability between different metadata standards and those essential for spatial data infrastructures;
3) Enhancement of information retrieval techniques for spatial data infrastructure using disambiguated vocabularies.

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