Spatial Data Mining: Theory and Application

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Data Mining Techniques

Metabolic Algorithm for Software Requirement Engineering p. Article Preview. Abstract: The spatial data mining is an important branch of data mining, this paper introduced the technology of spatial data mining based on GIS, the spatial data mining and the GIS integration of the steps and main mode are described.

Add to Cart. Advanced Materials Research Volume Main Theme:. Manufacturing Systems and Industry Application. Edited by:. Yanwen Wu. Online since:. June Chun Chang Fu , Nan Zhang. Cited by. Related Articles. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations.

The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology. Spatial science, also referred to as geographic information science, plays an important role in many scientific disciplines as it seeks to understand, analyze, and visualize real-world phenomena according to their locations.

Spatial scientists apply technologies such as geographic information systems GIS and remote sensing to spatial e. Tied to the current era of big data is the real-time generation of spatial big data, which have become ubiquitously available from geotagged social media posts on Twitter to environmental sensors collecting meteorological information [ 1 ]. Data science, and by extension spatial data science, are still evolving fields that provide methods to organize how we think about and approach generating new knowledge from spatial big data.

The scientific field of geospatial artificial intelligence geoAI was recently formed from combining innovations in spatial science with the rapid growth of methods in artificial intelligence AI , particularly machine learning e. The innovation of geoAI partly lies in its applications to address real-world problems.

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Department of Energy Oak Ridge National Laboratory Urban Dynamics Institute , which included advances in remote sensing image classification and predictive modeling for traffic. Further, the application of AI technologies for knowledge discovery from spatial data reflects a recent trend as demonstrated in other scientific communities including the International Symposium on Spatial and Temporal Databases.


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These novel geoAI methods can be used to address human health-related problems, for example, in environmental epidemiology [ 3 ]. In particular, geoAI technologies are beginning to be used in the field of environmental exposure modeling, which is commonly used to conduct exposure assessment in these studies [ 4 ]. Ultimately, one of the overarching goals for integrating geoAI with environmental epidemiology is to conduct more accurate and highly resolved modeling of environmental exposures compared to conventional approaches , which in turn would lead to more accurate assessment of the environmental factors to which we are exposed, and thus improved understanding of the potential associations between environmental exposures and disease in epidemiologic studies.

Spatial Data Mining I: Essentials of Cluster Analysis

Further, geoAI provides methods to measure new exposures that have been previously difficult to capture. The purpose of this commentary is to provide an overview of key concepts surrounding the emerging field of geoAI; recent advances in geoAI technologies and applications; and potential future directions for geoAI in environmental epidemiology.

Several key concepts are currently at the forefront of understanding the geospatial big data revolution. Big data, such as electronic health records and customer transactions, are generally characterized by a high volume of data; large variety of data sources, formats, and structures; and a high velocity of new data creation [ 5 — 7 ].

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Spatial Data Mining – Theory and Application – Bookyage

As a consequence, big data require specialized methods and techniques for processing and analysis. Data science broadly refers to methods to provide new knowledge from the rigorous analysis of big data, integrating methods and concepts from disciplines including computer science, engineering, and statistics [ 8 , 9 ]. The data science workflow generally resembles an iterative process of data import and processing, followed by cleaning, transformation, visualization, modeling, and finally communication of results [ 10 ].

Spatial data science is a niche and still forming field focused on methods to process, manage, analyze, and visualize spatial big data, providing opportunities to derive dynamic insights from complex spatial phenomena [ 11 ]. Spatial data science workflows are comprised of steps for data manipulation, data integration, exploratory data analysis, visualization, and modeling — and are specifically applied to spatial data often using specialized software for spatial data formats [ 12 ].

Research on Spatial Data Mining in E-Government Information System

For example, a spatial data science workflow may include data wrangling using open source solutions such as the Geospatial Data Abstraction Library GDAL , scripting in R, Python, and Spatial SQL for spatial analyses facilitated by high-performance computing e. Spatial data synthesis is considered an important challenge in spatial data science, which includes issues related to spatial data aggregation of different scales and spatial data integration harmonizing diverse spatial data types related to format, reference, unit, etc. Advances in cyberGIS defined as GIS based on advanced cyberinfrastructure and e-science — and more broadly high-performance computing capabilities for high-dimensional data — have played an integral role in transforming our capacity to handle spatial big data and thus for spatial data science applications.

For example, a National Science Foundation-supported cyberGIS supercomputer called ROGER was created in , which enables the execution of geospatial applications requiring advanced cyberinfrastructure through high-performance computing e.


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  6. As spatial data science continues to evolve as a discipline, spatial big data are constantly expanding, with two prominent examples being volunteered geographic information VGI and remote sensing. The term VGI encapsulates user-generated content with a locational component [ 14 ]. In the past decade, VGI has seen an explosion with the advent and continued expansion of social media and smart phones, where users can post and thus create geotagged tweets on Twitter, Instagram photos, Snapchat videos, and Yelp reviews [ 15 ]. Usage of VGI should be accompanied by an awareness of potential legal issues including but not limited to intellectual property, liability, and privacy for the operator, contributor, and user of VGI [ 16 ].

    Remote sensing is another type of spatial big data capturing characteristics of objects from a distance such as imagery from satellite sensors [ 17 ]. Depending on the sensor, remote sensing spatial big data can be expansive in both its geographic coverage spanning the entire globe as well as its temporal coverage with frequent revisit times. In recent years, we have seen an enormous increase in satellite remote sensing big data as private companies and governments continue to launch higher resolution satellites.

    For example, DigitalGlobe collects over 1 billion km 2 of high-resolution imagery each year as part of its constellation of commercial satellites including the WorldView and GeoEye spacecraft [ 18 ]. The U. Data science involves the application of methods in scientific fields such as artificial intelligence AI and data mining. AI refers to machines that make sense of the world, automating processes that create scalable insights from big data [ 5 , 20 ].

    Machine learning is a subset of AI that focuses on computers acquiring knowledge to iteratively extract information and learn from patterns in raw data [ 20 , 21 ]. Deep learning is a cutting-edge type of machine learning that draws inspiration from brain function, representing a flexible and powerful way to enable computers to learn from experience and understand the world as a nested hierarchy of concepts, where the computer is able to learn complicated concepts by building them from simpler concepts [ 20 ].

    Deep learning has been applied to natural language processing, computer vision, and autonomous driving [ 20 , 22 ].

    Quality Aspects in Spatial Data Mining

    Data mining refers to techniques to discover new and interesting patterns from large datasets such as identifying frequent itemsets in online transaction records [ 23 ]. Many techniques for data mining were developed as part of machine learning [ 24 ]. Applications of data mining techniques include recommender systems and cohort detection in social networks.

    Geospatial artificial intelligence geoAI is an emerging science that utilizes advances in high-performance computing to apply technologies in AI, particularly machine learning e. Featured geoAI applications included deep learning architectures and algorithms for feature recognition in historical maps [ 25 ]; multi-sensor remote sensing image resolution enhancement [ 26 ]; and identification of the semantic similarity in VGI attributes for OpenStreetMap [ 27 ].

    For example, AI research has been presented at the International Symposium on Spatial and Temporal Databases, which features research in spatial, temporal, and spatiotemporal data management and related technologies. Given the advances and capabilities on display in recent research, we can begin to connect the dots regarding how geoAI technologies can be specifically applied to environmental epidemiology.

    To determine the factors to which we may be exposed and thus may influence health, environmental epidemiologists implement direct methods of exposure assessment, such as biomonitoring e. Exposure modeling involves the development of a model to represent a particular environmental variable using various data inputs such as environmental measurements and statistical methods such as land use regression and generalized additive mixed models [ 28 ]. Exposure modeling is a cost-effective approach to assess the distribution of exposures in particularly large study populations compared to applying direct methods [ 28 ].

    Exposure models include basic proximity-based measures e. Spatial science has been critical in exposure modeling for epidemiologic studies over the past two decades, enabling environmental epidemiologists to use GIS technologies to create and link exposure models to health outcome data using geographic variables e. For example, previous exposure modeling efforts have often been associated with coarse spatial resolutions, impacting the extent to which the exposure model is able accurately estimate individual-level exposure i.

    Advances in geoAI enable accurate, high-resolution exposure modeling for environmental epidemiologic studies, especially regarding high-performance computing to handle big data big in space and time; spatiotemporal as well as developing and applying machine and deep learning algorithms and big data infrastructures to extract the most meaningful and relevant pieces of input information to, for example, predict the amount of an environmental factor at a particular time and location.

    A spatial data mining approach using machine learning and OpenStreetMap OSM spatial big data was developed to enable selection of the most important OSM geographic features e. The algorithm next trained a random forest model a popular machine learning method using decision trees for classification and regression modeling to generate the relative importance of each OSM geographic feature.

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    The arrangement method of raster data can be described in table 1 and figure 5. Each star-type model includes a fact table and some corresponding dimension tables. All the fact tables and dimension tables are stored in the SQL Server database. The key of constructing a star-type model is to design right fact table and dimension table as well as establish mutual connections between them[ 14 ].