1 A User-Friendly Data Mining System J. Raul Ramirez, Ph.D. The Ohio State University Center for Mapping 1. Introduction Image acquisition of the Earth's surface has become a common event. LANDSAT, SPOT, IRS, IKONOS, are some examples of satellite platforms that are continuously collecting images of the surface of the Earth. Also, hundreds of aerial photo missions are flown each year all over the Earth to acquire images of its surface. With all this activity there is no shortage of images of the Earth today. In general, the use of these images is limited to a small sector of the population: those scientists with a background in geo-science. A major reason for the small number of users is the fact that is not easy to use these images. Usually, they are stored in large computer files, requiring specialized software to display and manipulate them and an understanding of what they represent. Versions of software necessary to display and manipulate these images are becoming more and more common today. But, even if the ordinary person could display these images, it is very difficult to find specific information of interest to the user. There is very little explicit information (the kind of information a computer can use) in these images. Most of the information in these images is implicit. As a consequence, these images are used mostly in scientific projects. This paper describes our ongoing research in developing a user-friendly approach to retrieve user-selected information from these images with minimum user effort. We are designing a data mining system for Earth images. Data mining (also known as Knowledge Discovery in Databases - KDD) is defined by Frawley et. al. (1991) as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data." Conventionally, it uses machine learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans. Our data mining system will allow the general public to retrieve information from Earth images with very little effort and with only minimum information. This is accomplished by developing and implementing the idea of graphic metadata (graphic data about data), by using fuzzy logic as part of the search engine, by using machine learning, and by using the logic used in navigation and orientation of daily tasks. Our data mining system will be Internet based and is tested with Landsat 7 data, but the system is developed in such a way that it could be used with any kind of imagery. 2. Data Mining and Images of the Earth's Surface There are many definitions of data mining. Grossman (1999) indicates, "Data mining is the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. That is, data mining attempts to extract knowledge from data." In the Zucker-Kodratoof Data mining Glossary (1998) data mining is defined as: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotion, and credit risk analysis. Simply put, data mining is basically a modeling activity. You need to describe the data, build a predictive model describing a situation you want to investigate based on patterns determined from known results, and verify the model. Once these things are done, the model is used to test the data to see what portions of the data satisfy the model. If you find that the model is satisfied, you have discovered something new about your data that is of value to you.
2 Today, data mining activities are concentrated mainly in tabular data (Elder and Abbot, 1998), (Piatetsky- Shapiro, 1999), (Graettinger, 1999), and in a lesser quantity in text data (Hearst, 1999). We did not find any application of data mining to images but Grossman (1999) mentioned the use of data mining for images as a new application under "multi-media documents". In this research we follow closely the general concept of data mining. We build models of objects to be found, then we will test the data for those objects, and if we find them, we will extract them. Our specific approach is described below. 3. Development of Our User-Friendly Data Mining System We are investigating the following topics: (1) Navigation and directions taxonomy (2) Fuzzy operators (3) The model for data mining of images: Graphic Metadata (4) Database design for graphic metadata (5) Data mining system design A brief discussion of topics (1), and (2), and a more in-depth discussion of topics (3) and (4) are presented next. Topic (5) is not discussed in this paper and it is part of our ongoing research. 3.1 Navigation and Directions Taxonomy We have investigated the terms and facts that are most commonly used for navigation and giving directions. We were interested in learning what are commonly the terms and facts most frequently used in everyday situations to provide geographic directions. For example, if you need to go from Point A to Point B in a city, and you need to ask for directions, how are these directions provided? How is this done if you need to go from City A to City B? Etc. Our findings are similar to those found in the literature. In general, geographic directions are given using geographic landmarks, their names, approximate distances, and basic direction commands: straight forward, turn left, turn right, etc. 3.2 Fuzzy Operators We have investigated a set of operators to express these terms and facts. Our goal was transforming these terms and facts into computer-based operators able to reflect the way people provide directions. Generally, we have investigated three types of operators: (1) For fully defined terms and facts, for example, starting at point X follow highway Y for two miles, then exit at point Z; (2) Incompletely defined terms and facts, Figure 1. Fuzzy membership function for around 2 miles
3 for example, when near to point X, follow a highway for several miles then exit around point Z; and (3) Mixed expressions, for example, at point X take a highway for several miles and exit at Z. We have used Fuzzy Logic to derive these operators as the theory of Fuzzy Logic provides techniques to handle such imprecise, vague and ill-defined statements. For example, near point X, around point Z and around 2 miles, are fuzzy terms for which equivalent fuzzy operators can be derived. These operators can be characterized by using appropriate fuzzy membership functions. Figure 1 shows a membership function based on a Gaussian error density function that can be used to define the phrase around 2 miles. Similarly, other vague phrases can be defined using different fuzzy membership functions. When a decision needs to be made by combining vague phrases using logical operators such as AND, OR, NOT etc., fuzzy aggregation operators can be used to combine them. 3.3 The Model for Data Mining of Images: Graphic Metadata Design As it was indicated earlier, data mining requires the use of models to find hidden information in a database. We believe that data mining of images also requires such models. Models are built based on hypotheses and facts. For example, if a TV cable provider starts offering Internet cable connection and it wants to increase its current base of Internet cable connection users, the provider could use data mining to select possible new users. In order to do that, the provider needs to build a model describing users of Internet cable connection. By selecting a set of parameters, such as annual income, level of education, credit history, etc. and by comparing and analyzing the current users of Internet cable connection, the provider will build the model. Then, the model will be run on all the provider's TV cable users, and the outcome will be a list of those individuals that may be interested in using the Internet cable connection service. Then, the provider can target them in an ad campaign. We should notice that in the case of tabular information, the model is based on a set of known facts and assumptions. This allows the construction of generic models. In the case of images, we could think of two different approaches to build the models. In the first approach, we build generic models. For example, a road could be described based on the number of lanes, the width of each lane, the type of geometric alignments, the surrounding geographic features, etc. The drawback of this type of model is that because the restricted explicit information to find roads in images requires an exhaustive search of the whole image, and because of shadows and other circumstances, it may be possible that not all roads of that type are found. Besides, the user may be interested in a very specific road and not in all the roads of this type. In the second approach, models are built for specific terrain features. In this case, one of the parameters to be used is the location of the feature. In general, these two models (generic and specific) complement each other. We believe that from the viewpoint of images, you would need to start with specific models (because the limited explicit information on the images), and later on to use generic models to refine the search of information. This is the approach we are following in this research. Specific models need to carry explicit information about the content of the images. Vector landmarks and their names could be used to build the type of specific models we could use for image data mining. Vector landmarks are the computer representation (in vector format) of those terrain features that are well known in a region, such as major highways, buildings, sport facilities, etc. Vector landmarks and their names could be connected to images in order to facilitate the location of specific objects on those images. How to geodefine those landmarks and their respectively names, and how to connect those landmarks and their names to the images, in an efficient way, are open questions. Most users of geo-spatial data are familiar with the word Metadata (which is data about data). In this context we will call Graphic Metadata the kind of data we believe is needed to help the search of images. Graphic metadata is geo-referenced data about images. As it was indicated above, it is a very rough representation of the ground, with a minimum number of attributes about the images. Graphic metadata is the specific model to be used in data mining of images. As it was indicated earlier, digital images by themselves carry very little explicit information. The three pieces of explicit information carried by each pixel of a digital image are the row and column of its location in the image, and an attribute value. Usually, the attribute value is a color code. The only way to locate a particular feature or area on an image is to geo-reference all the pixels of the image and search the image
4 based on coordinate values. This type of approach requires knowledge of geo-spatial science and familiarity with the region in question beyond the general public knowledge. To find a particular object such as a road without an exhaustive search of the image is impossible, and even an exhaustive search may not always be successful. The fundamental issue here is images do not carry enough explicit information to allow computers to locate complex objects on them. Our goal in this part of the research is to define the minimum set of characteristics for the type of graphic metadata that will allow us to find objects on images. In other words, we are defining the characteristics of the specific model for data mining of images. If vector datasets exist for the same area as the images, an obvious solution would be to use the vector dataset as the specific model for image data mining. This could be accomplished by geo-referencing both datasets and using the vector information as the base for locating the area to be searched on the images. We recognize that the kind of vector data and attributes needed to help search images does not necessarily carry the same type of accuracy and completeness as conventional vector data. We believe that from the point of view of the specific model for image data mining, all that it is needed is a very rough vector representation of the landmarks, without any type of symbolization, and the corresponding landmark names. A basic condition to using graphic metadata is the existence of a vector data set for the region in consideration. In the case of the United States, there is a large digital coverage at scale 1:100,000. There are two versions of this data, one from the U.S. Geological Survey (DLG files), and the other one from the U.S. Bureau of the Census (TIGER files). These data sets can be complemented with information from the Geographic Names Information System (GNIS), developed by the USGS in cooperation with the U.S. Board on Geographic Names (BGN). GNIS contains information about almost 2 million physical and cultural geographic features in the United States. The Federally recognized name of each feature described in the database is identified, and state, county, and geographic coordinates describing a feature s location are also given. The GNIS is our Nation's official repository of domestic geographic names information. The following information about a selected geographic feature can be obtained from GNIS: Federally recognized feature name, Feature type, Elevation (where available), Estimated 1994 population of incorporated cities and towns, State(s) and county(s) in which the feature is located, Latitude and longitude of the feature location, List of USGS 7.5-minute x 7.5-minute topographic maps, on which the feature is shown, Names, other than the federally recognized name, by which the feature may now or have been known. The GINS dataset was developed from the 1:24,000-scale quadrangle series, but its positional accuracy is inferior. Our graphic metadata is developed as follows: linear features, such as boundaries, roads, railroads, miscellaneous transportation features, and hydrographic features are taken from the 1:100,000 DLG or TIGER files. Area features such as parks and cemeteries are taken from TIGER files. Point features are taken from the TIGER files and GNIS database. Geographic names are taken from GNIS and the TIGER files. All these datasets are integrated into a consistent database structure. The development of this database structure will be described later. We have collected all the above data sources for a pilot project area in Ohio. As indicated above, there are two models for the data mining of images: a specific and a generic model. Graphic metadata is the specific model and will help to locate regions on the images where well-known ground objects are represented. If the user is interested in objects that are not well known, then generic
5 models need to be used. Generic models combined with image processing techniques can be used to find these types of objects. 3.4 Database Design for Graphic Metadata At The Ohio State University Center for Mapping we have developed a database system for efficient storage, retrieval, and manipulation of geographic data. Ramirez, Fernandez-Falcon, and Schmidley (1993) described this system known, as the Center for Mapping Database (CFMDBF), and Bidoshi (1995) used it for comparing different datasets of the same area. The Center For Mapping Database Format (CFMDBF) has unique capabilities important for the storage, retrieval, and manipulation of geospatial data. These capabilities include cartographic features with opaque/transparent segments, coordinate systems of n-dimensions, optimization of storage space, expandability, fast feature location tools, and expression of features in a raster-like environment using 3-D Freeman Code (Ramirez, 2001). The CFMDBF is a vector database with raster characteristics. Partitioning the vector area into grid cells incorporates the raster characteristics. The grid cells are defined as an exact number of cells fitting the vector area of interest. Data manipulation and retrieval becomes easier using a combination of both data structures (raster and vector). Some highlights of the CFMDBF are described next Figure I L2 III 35 Arc3II Arc4 11 IV Network of Grids & Cell Covering the Vector Data Creation of a Network of Grids and Cells Covering Vector Area To get some specific information of interest by searching all the vector data is inefficient. If the whole vector data area is partitioned into tiles, the extraction of information will be much easier because the scope of query is reduced into one or several tiles. Therefore, in establishing the CFMDBF it is necessary to create a network of grids and cells covering the vector data area. The method consists in partitioning the area into quadrants called grids. The cell is completely regular and is not finer in the denser regions of data. Then, according to the map condition we group a specific number of grids together into one cell (see Figure 2, with cells I, II, III, and IV). The relational database derived from such simplicity and regularity is apparent. The location of each grid can be computed from its row and column numbers. Then, every feature will have a grid representation and a vector representation. A cell identification number also is allocated to each cell, and this cell number together with information of all the features that are fully contained or intersect the cell are stored in the database.
6 With the raster representation of the feature, the scope of the search is not only reduced but also make simple to determine the location and shape of the entire feature. Vector data make every cell and grid meaningful and allow quick query and accurate calculation of the feature information. Therefore, the advantage of this method is this simplicity, resulting in shorter processing time as well as easier software development and maintenance Database Structure CFMDBF is organized in the form of tables. A table is composed of blocks. Blocks are comprised of one or more records. A record is a collection of fields. Fields contain sub-fields. Sub-fields are the minimum unit in the CFMDBF. Records of a given table have fixed size. Tables have two types of records: header and data records. Header is divided into common (with the same structure for all the tables in the database format) and individual (which has unique structure for a given table). Headers carry information that allows the table to be related to its project. Tables are grouped into four categories: setup, class, data, and description. The detail information about these tables is shown below. Setup tables: carry information on how the database user wants to organize the data. Feature Type Table Element Type Table Feature Composition Table The Data Identification and Setup Tables Stores the type of each feature along with its ID. Stores the element type (as point, line) along with the ID the element. Stores IDs from two previous tables along with a Table ID given by system. Relate Table IDs from the previous table with table names. Class Tables: carry information to access and manipulate the data. Feature Table Data Table Cell Table Cell Key Table Stores data using the Feature ID and the Element ID, relating them with the Table ID. Contains information necessary to access the coordinates and attributes of any element. Carries the unique ID of elements intersecting or contained in a cell. Carries information to access each cell of the grid covering the whole area of the map. Data Tables: carry the data Vector Data Table Grid Data Table Attribute Data Table Carries the geometric coordinate values of any element. Carries the grid representation of the elements that are a combination of grid values and Freeman code. Contains attribute information for the point, curve, text and line elements in the database.
7 Description Tables: are related to the characteristics of the elements. Description Tables contain information such as: Coordinate Axis Description, Reference System, Projection System, Coordinate Units, Primary Color, Line Style, Line Patterning, Level Description, Font Style, Text Justification, and Special Field Codes Example of the CFMDBF The following tables refer to Figure 2. Feature Point Table Feature Table Grid Table Road River Building F 121 L 35 Record BP Feature_ID Feature_Name Lo_Grid Lo_Vector FP Lane Avenue Federal Building Lo_Grid gives the initial location and length in the Grid table of the features Lo_Vector gives the initial location and length in the Vector Data Table. Record BP Grid Location FP ,1; 3,1; 3, ,2; 4,3; 5,3; 5,4; ,5; 6,5; 6, ,5; 4,6; 4, ,7; 2,7; 2,6; 2, ,5 0 Vector Data Table Record BP Coordinate Value FP xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy 0... Cell Point Table Record Cell_ID Lo_Cell
8 Cell Table Lo_Cell gives the initial location in the Cell Table of each cell. Record BP Feature_ID Lo_Grid Length1 Lo_Vector Length2 FP Summary and Conclusions of CFMDBF The structure of the CFMDBF takes advantage of the major benefits of the raster and the vector data structures. The manipulation, updating, conflation, extraction, and query of data become easier and faster. And, CFMDBF can be used in many situations according to different requirements. Users can select the tables that are appropriate to their needs or establish new tables to accommodate any special need. 4. Conclusions and Future Work Based on the idea of graphic metadata, and using fuzzy logic, and an efficient database structure we are implementing user-friendly data mining for images. Graphic metadata adds explicit information to images; fuzzy logic allows searching information in a similar fashion as humans deal with geospatial information and directions; and the CFMDBF provides the searching speed that it is required for real-time queries. All these components are in place together with the data for a pilot project in Ohio. We are currently working on the user interface and visualization system. References Bidoshi, K., A Database System as a Tool for Comparing, Updating, and Conflating Spatial Data from DLG and GPSVan, Unpublished Thesis, The Ohio State University, Elder, J.F., Abbot, D.W. "A Comparison of Leading Data mining Tools," Fourth International Conference on Knowledge Discovery & Data Mining, New York, Frawley, W.J., Piatetsky-Shapiro, G., and Matheus, C.J. "Knowledge Discovery in Databases: An Overview," AAAI/MIT Press, Graetting, T. "Digging Up $$$ with Data Minining," The Data administrator Newsletter, Grossman, R. (Editor) "Data mining Research: Opportunities and Challenges: A report of Three NSF Workshops on Mining Large, Massive, and Distributive Data," University of Illinois, Chicago, Hearst, M.A. "Untangling Text Data Mining," Proceedings of ACL'99: the 37 th Annual Meeting of the Association of Computer Linguistics, Maryland, Two Crows Corporation, "Zucker-Kodratoff Data Mining Glossary", Two Crows Corp., Maryland, Two Crows Corporation, Introduction to Data Mining and Knowledge Discovery, Two Crows Corp., Maryland, Ramirez, An Extended Representation Model for Geographic Data, Proceedings 20 th International Cartographic Conference, Beijing, Ramirez, J.R., Fernandez-Falcon, E., Schmidley, R., Design of The Center for Mapping Database Format, Center for Mapping Internal Report, August 1992.
Introduction to GIS (Basics, Data, Analysis) & Case Studies 13 th May 2004 Content Introduction to GIS Data concepts Data input Analysis Applications selected examples What is GIS? Geographic Information
A Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. GIS allows us to view,
What is GIS? Geographic Information Systems Introduction to ArcGIS A database system in which the organizing principle is explicitly SPATIAL For CPSC 178 Visualization: Data, Pixels, and Ideas. What Can
Introduction to GIS http://libguides.mit.edu/gis 1 Overview What is GIS? Types of Data and Projections What can I do with GIS? Data Sources and Formats Software Data Management Tips 2 What is GIS? 3 Characteristics
Lecture 3: Models of Spatial Information Introduction In the last lecture we discussed issues of cartography, particularly abstraction of real world objects into points, lines, and areas for use in maps.
Adaptation of High Resolution Ikonos Images to Googleearth for Zonguldak Test Field Umut G. SEFERCIK, Murat ORUC and Mehmet ALKAN, Turkey Key words: Image Processing, Information Content, Image Understanding,
GIS Digital Humanities Boot Camp Series GIS Fundamentals GIS Fundamentals Definition of GIS A geographic information system (GIS) is used to describe and characterize spatial data for the purpose of visualizing
Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Advanced Image Management using the Mosaic Dataset Vinay Viswambharan, Mike Muller Agenda ArcGIS Image Management
Introduction to GIS 1 Introduction to GIS http://www.sli.unimelb.edu.au/gisweb/ Dr F. Escobar, Assoc Prof G. Hunter, Assoc Prof I. Bishop, Dr A. Zerger Department of Geomatics, The University of Melbourne
LIBRARY OF CONGRESS COLLECTIONS POLICY STATEMENTS ±² Collections Policy Statement Index Cartographic and Geospatial Materials This document consolidates and replaces the former Maps, Atlases, and Remote
REGIONAL SEDIMENT MANAGEMENT: A GIS APPROACH TO SPATIAL DATA ANALYSIS Lynn Copeland Hardegree, Jennifer M. Wozencraft 1, Rose Dopsovic 2 ABSTRACT: Regional sediment management (RSM) requires the capability
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal
v Software Release Notice -. Acquired Software 1. Software Name: Software Version: ArcView GIs@ 3.3 2. Software Function: ArcView GIS 3.3, developed by Environmental Systems Research Institute, Inc. (ESRI@),
Raster Data Structures Tessellation of Geographical Space Geographical space can be tessellated into sets of connected discrete units, which completely cover a flat surface. The units can be in any reasonable
A HYBRID APPROACH FOR AUTOMATED AREA AGGREGATION Zeshen Wang ESRI 380 NewYork Street Redlands CA 92373 Zwang@esri.com ABSTRACT Automated area aggregation, which is widely needed for mapping both natural
Assessing the implementation of Rawalpindi s Guided Development Plan through GIS and Remote Sensing Muhammad Adeel (Lecturer Muhammad Adeel, Institute of Geographical Information Systems, NUST, H-12 Islamabad,
DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,
KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS COLLEGE OF ENVIRONMENTAL DESIGN CITY & RIGINAL PLANNING DEPARTMENT TERM ROJECT Implementing GIS in Optical Fiber Communication By Ahmed Saeed Bagazi ID# 201102590
SESSION 8: GEOGRAPHIC INFORMATION SYSTEMS AND MAP PROJECTIONS KEY CONCEPTS: In this session we will look at: Geographic information systems and Map projections. Content that needs to be covered for examination
Getting the Map into the Computer Getting the Map into the Computer Getting Started with Geographic Information Systems Chapter 4 4.1 Analog-to-Digital Maps 4.2 Finding Existing Map Data 4.3 Digitizing
Geocoding in Law Enforcement Final Report Geocoding in Law Enforcement Final Report Prepared by: The Crime Mapping Laboratory Police Foundation August 2000 Report to the Office of Community Oriented Policing
CENTRAL OREGON COMMUNITY COLLEGE: GEOGRAPHIC INFORMATION SYSTEM PROGRAM 1 CENTRAL OREGON COMMUNITY COLLEGE Associate Degree Geographic Information Systems Program Outcome Guide (POG) Program Outcome Guide
Institute of Natural Resources Departament of General Geology and Land use planning Work with a MAPS Lecturers: Berchuk V.Y. Gutareva N.Y. Contents: 1. Qgis; 2. General information; 3. Qgis desktop; 4.
3-D Object recognition from point clouds Dr. Bingcai Zhang, Engineering Fellow William Smith, Principal Engineer Dr. Stewart Walker, Director BAE Systems Geospatial exploitation Products 10920 Technology
Retrieving and Unpacking SDTS Data Tutorial and Users Manual June 23, 1998 Neither the U.S. Government nor any agency thereof nor any of their employees make any warranty, expressed or implied, or assume
Reading Questions Week two Lo and Yeung, 2007: 2 19. Schuurman, 2004: Chapter 1. 1. What distinguishes data from information? How are data represented? 2. What sort of problems are GIS designed to solve?
Improving Data Mining of Multi-dimension Objects Using a Hybrid Database and Visualization System Yan Xia, Anthony Tung Shuen Ho School of Electrical and Electronic Engineering Nanyang Technological University,
Earth Data Science in The Era of Big Data and Compute E. Lynn Usery U.S. Geological Survey firstname.lastname@example.org http://cegis.usgs.gov U.S. Department of the Interior U.S. Geological Survey Board on Earth Sciences
Introduction The following document is intended to provide a basic understanding of raster data. Raster data layers (commonly referred to as grids) are the essential data layers used in all tools developed
INTRODUCTION TO ARCGIS SOFTWARE I. History of Software Development a. Developer ESRI - Environmental Systems Research Institute, Inc., in 1969 as a privately held consulting firm that specialized in landuse
Subcommittee for Base Cartographic Data Federal Geographic Data Committee Federal Geographic Data Committee Department of Agriculture Department of Commerce Department of Defense Department of Energy Department
Spatial Data Mining Methods and Problems Abstract Use summarizing method,characteristics of each spatial data mining and spatial data mining method applied in GIS,Pointed out that the space limitations
Implementation Planning presented by: Tim Haithcoat University of Missouri Columbia 1 What is included in a strategic plan? Scale - is this departmental or enterprise-wide? Is this a centralized or distributed
GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION GIS Syllabus - Version 1.2 January 2007 Copyright AICA-CEPIS 2009 1 Version 1 January 2007 GIS Certification Programme 1. Target The GIS certification is aimed
Cloud-based Geospatial Data services and analysis Xuezhi Wang Scientific Data Center Computer Network Information Center Chinese Academy of Sciences 2014-08-25 Outlines 1 Introduction of Geospatial Data
WHAT IS GIS - AN INRODUCTION GIS DEFINITION GIS is an acronym for: Geographic Information Systems Geographic This term is used because GIS tend to deal primarily with geographic or spatial features. Information
GIS MAPPING FOR IRRIGATION DISTRICT RAPID APPRAISALS Daniel J. Howes 1, Charles M. Burt 2, Stuart W. Styles 3 ABSTRACT Geographic information system (GIS) mapping is slowly becoming commonplace in irrigation
How to Download Census Data from American Factfinder and Display it in ArcMap Factfinder provides census and ACS (American Community Survey) data that can be downloaded in a tabular format and joined with
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
Bentley ArcGIS Connector Introduction ESRI, as a GIS software products company, and Bentley Systems, Incorporated, as a developer of solutions for architecture/engineering/construction (AEC) professionals,
Tradeoff Studies about Storage and Retrieval Efficiency of Boundary Data Representations for LLS, TIGER and DLG Data Structures David Clutter and Peter Bajcsy National Center for Supercomputing Applications
Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite R.Manonmani, G.Mary Divya Suganya Institute of Remote Sensing, Anna University, Chennai 600 025
What you can do:...3 Data Entry:...3 Drillhole Sample Data:...5 Cross Sections and Level Plans...8 3D Visualization...11 W elcome to North Face Software s software. With this software, you can accomplish
Visualizing of Berkeley Earth, NASA GISS, and Hadley CRU averaging techniques Robert Rohde Lead Scientist, Berkeley Earth Surface Temperature 1/15/2013 Abstract This document will provide a simple illustration
Adobe Insight, powered by Omniture Accelerating government intelligence to the speed of thought 1 Challenges that analysts face 2 Analysis tools and functionality 3 Adobe Insight 4 Summary Never before
Implementing an Imagery Management System at Mexican Navy The Mexican Navy safeguards 11,000 kilometers of Mexican coastlines, inland water bodies suitable for navigation, and the territorial sea and maritime
User s Guide to ArcView 3.3 for Land Use Planners in Puttalam District Dilhari Weragodatenna IUCN Sri Lanka, Country Office Table of Content Page No Introduction...... 1 1. Getting started..... 2 2. Geo-referencing...
1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful
Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum Siva Ravada Senior Director of Development Oracle Spatial and MapViewer 2 Evolving Technology Platforms
APPLY EXCEL VBA TO TERRAIN VISUALIZATION 1 2 Chih-Chung Lin( ), Yen-Ling Lin ( ) 1 Secretariat, National Changhua University of Education. General Education Center, Chienkuo Technology University 2 Dept.
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
ADWR GIS Metadata Policy 1 PURPOSE OF POLICY.. 3 INTRODUCTION.... 4 What is metadata?... 4 Why is it important? 4 When to fill metadata...4 STANDARDS. 5 FGDC content standards for geospatial metadata...5
LIBER QUARTERLY, ISSN 1435-5205 LIBER 1999. All rights reserved K.G. Saur, Munich. Printed in Germany Digital Cadastral Maps in Land Information Systems by PIOTR CICHOCINSKI ABSTRACT This paper presents
Developing Fleet and Asset Tracking Solutions with Web Maps Introduction Many organizations have mobile field staff that perform business processes away from the office which include sales, service, maintenance,
JACIE Science Applications of High Resolution Imagery at the USGS EROS Data Center November 8-10, 2004 U.S. Department of the Interior U.S. Geological Survey Michael Coan, SAIC USGS EROS Data Center email@example.com
Intergraph Roadway Information Management Solution A White Paper Security, Government & Infrastructure, a division of Intergraph Title Title Title Title Table of Contents 1. Introduction... 1 2. Intergraph
14 Spatial Data Analysis OVERVIEW This chapter is the first in a set of three dealing with geographic analysis and modeling methods. The chapter begins with a review of the relevant terms, and an outlines
Grade Level: K - 8 Subject: Motion Prep Time: < 10 minutes Duration: 30 minutes Objective: To learn how to analyze GPS data in order to track an object and derive its velocity from positions and times.
Google Earth Digitale Wege in eine bekannte Welt Sep. 2006 Joachim Glaubrecht Google Enterprise firstname.lastname@example.org What is Google Enterprise? 2 1 Focus.de: "Der Papst-Besuch in Google Earth" 3 How to Geo
Data Visualization Prepared by Francisco Olivera, Ph.D., Srikanth Koka Department of Civil Engineering Texas A&M University February 2004 Contents Brief Overview of ArcMap Goals of the Exercise Computer
Mapping Mashup/Data Integration Development Resources David Hart GIS Specialist University of Wisconsin Sea Grant Institute October 6, 2008 Virtual Globes A virtual globe is a 3D software model or representation
GIS: Geographic Information Systems A short introduction Outline The Center for Digital Scholarship What is GIS? Data types GIS software and analysis Campus GIS resources Center for Digital Scholarship
Standard on Digital Cadastral Maps and Parcel Identifiers APPROVED JULY 2003 INTERNATIONAL ASSOCIATION OF ASSESSING OFFICERS 1. Scope This standard provides recommendations on the development and maintenance
Page 1 of 10 GIS 101 - Introduction to Geographic Information Systems Last Revision or Approval Date - 9/8/2011 College of the Canyons SECTION A 1. Division: Mathematics and Science 2. Department: Earth,
Geographic Information Systems In Emergency Management Prepared by: John C. Pine, Ed.D., Associate Professor, Institute for Environmental Studies, Louisiana State University, Baton Rouge, LA. Introduction
A GENERAL APPROACH TO MAP CONFLATION Anthony E. Lupien William H. Moreland Environmental Systems Research Institute Redlands, California 92373 Abstract Map conflation is approached as two generic geoprocessing
Coastal Change Analysis Lesson Plan COASTAL MONITORING & OBSERVATIONS LESSON PLAN Do You Have Change? NOS Topic Coastal Monitoring and Observations Theme Coastal Change Analysis Links to Overview Essays
Technology Trends In Geoinformation Dato Prof. Sr Dr. Abdul Kadir Bin Taib Department of Survey and Mapping Malaysia (JUPEM) Email: email@example.com www.jupem.gov.my NGIS 2008 3 rd. National GIS Conference
CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation
Map Matching and Real World Integrated Sensor Data Warehousing www.nrel.gov/tsdc www.nrel.gov/fleet_dna Evan Burton Data Engineer (Presenter) Jeff Gonder Vehicle System Analysis Team Lead Adam Duran Engineer/Analyst
Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Introduction to Imagery and Raster Data in ArcGIS Simon Woo slides Cody Benkelman - demos Overview of Presentation
Guidance for Flood Risk Analysis and Mapping Changes Since Last FIRM May 2014 This guidance document supports effective and efficient implementation of flood risk analysis and mapping standards codified
Presented at Short Course VII on Exploration for Geothermal Resources, organized by UNU-GTP, GDC and KenGen, at Lake Bogoria and Lake Naivasha, Kenya, Oct. 27 Nov. 18, 2012. GEOTHERMAL TRAINING PROGRAMME
White Paper A Visualization is Worth a Thousand Tables: How IBM Business Analytics Lets Users See Big Data Contents Executive Summary....2 Introduction....3 Too much data, not enough information....3 Only
3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension R.Queen Suraajini, Department of Civil Engineering, College of Engineering Guindy, Anna University, India, firstname.lastname@example.org
Intermodal Freight GIS Network Risk Assessment Final Report Introduction Previous Center for Intermodal Freight Transportation Studies (CIFTS) projects funded the development of an intermodal freight transportation
Sign Inventory and Management (SIM) Program Introduction A Sign Inventory and Management (SIM) Program is an area of asset management that focuses specifically on creating an inventory of traffic signs
1 Oscar Gobbato email@example.com firstname.lastname@example.org WFP Liberia Country Office GIS training - Summary Objectives 1 To introduce to participants the basic concepts and techniques in using Geographic
DATABASE MANAGEMENT Last day we looked at spatial data structures for both vector and raster data models. When working with large amounts of data, it is important to have good procedures for managing the
Introduction to ILWIS 3.11 using a data set from Kathmandu valley. C.J. van Westen Dept. of Environmental System Analysis, International Institute Geo-Information Science and Earth Observation (ITC), P.O.
Government 98dn Mapping Social and Environmental Space LAB EXERCISE 5: The Analysis of Fields Objectives of this lab: Visualizing raster data Using Spatial Analyst functions to create new data Analysis
Extend Table Lens for High-Dimensional Data Visualization and Classification Mining CPSC 533c, Information Visualization Course Project, Term 2 2003 Fengdong Du email@example.com University of British Columbia
Finding Data There are various ways to find data using the Hennepin County GIS Open Data site: Type in a subject or keyword in the search bar at the top of the page and press the Enter key or click the