Intro to advanced GIS and a review of basic GIS

Outlines About the class setting Materials to be covered and scheduled Quick review of GIS basics First lab (Lab 1)

What covered in introGIS Geospatial Tech GIS GIS data GIS data type GIS data format

GIS a simplified view of Earth Two types of coordinate systems Geographic coordinate system Projected coordinate system Conic, cylindrical, Azimuthal Distortions (shape, size, distance, direction) Two important things Define Project

Geographic Coordinate System Unprojected

Projected Coordinate System

What is GIS ? A computer system for - collecting, - storing, - manipulating, - analyzing, - displaying, and - querying geographically related information. GIS is a popular technology, but what exactly is it? What does it do? Basically, a geographic information system (GIS) is a computer-based tool for solving problems. A GIS integrates information in a way that helps us understand and find solutions to problems. Data about real-world objects is stored in a database and dynamically linked to an onscreen map, which displays the real-world objects. When the data in the database changes, the map updates to reflect the changes. In general, people use a GIS for four main purposes: data creation, data display, analysis, and output. You can display objects according to the data in your database (this is a powerful feature that you'll appreciate later). GIS analysis tools allow you to do things like find out how far your best customers travel to visit your store, which land parcels are within a flood zone, and which soil type is best for growing a particular crop. Output options include cartographic-quality maps as well as reports, lists, and graphs. Many different definitions of GIS have evolved in different areas and disciplines the information is always geographic or spatial Four components of any GIS: input; storage/retrieval; analysis; display Used in a steadily growing number of fields/disciplines/projects Origins lie (way back) in thematic maps; manual overlay “GIS” refers to the application or the software; “doing GIS” refers to increasing number of things Need understanding of GIScience to do GIS work.

In general GIS cover 3 components Computer system Hardware Computer, plotter, printer, digitizer Software and appropriate procedures Spatially referenced or geographic data People to carry out various management and analysis tasks

Geographic Data Geospatial data tells you where it is and attribute data tells you what it is. Metadata describes both geospatial and attribute data. In GIS, we call geographic data as GIS data or spatial data

1. Geospatial data

Traditional method To represent the geographic data is paper-based maps Geology map Topographic map City street map (we still use it a lot) ...

Characteristics of spatial data “mappable” characteristics: Location (coordinate system, will be lectured later) Size is calculated by the amount (length, area, perimeter) of the data Shape is defined as shape (point, line, area) of the feature Discrete or continuous Spatial relationships

Discrete and continuous Discrete data are distinct features that have definite boundaries and identities A district, houses, towns, agricultural fields, rivers, highways, … Continuous data has no define borders or distinctive values, instead, a transition from one value to another Temperature, precipitation, elevation, ...

GIS: a simplified view of the real world Points Lines Areas Networks A series of interconnecting lines Road network River network Sewage network Surfaces Elevation surface Temperature surface Discrete features Continuous features

Problems caused by the simplified features may still exist, but let’s live on it Dynamic nature (not static) Forest grow River channel change City expand or decline Identification of discrete and continuous features Road to be a line or a area? Scale Some may not fit to any type of features: fuzzy boundaries Transition area between woodland and grassland Lets do not worry about these problems now!!! Just keep in mind

Topology needed A collection of numeric data which clearly describes adjacency, containment (coincidence), and connectivity between map features and which can be stored and manipulated by a computer. A set of rules on how objects relate to each other Major difference in file formats Higher level objects have special topology rules

Two basic data models to represent these features Raster spatial data model Define space as an array of equally sized cells arranged in rows and columns. Each cell contains an attribute value and location coordinates Individual cells as building blocks for creating images of point, line, area, network and surface Continuous raster Numeric values range smoothly from one location to another, for example, DEM, temperature, remote sensing images, etc. Discrete raster Relative few possible values to repeat themselves in adjacent cells, for example, land use, soil types, etc. Vector spatial data model Use x-, y- coordinates to represent point, line, area, network, surface Point as a single coordinate pair, line and polygon as ordered lists of vertices, while attributes are associated with each features Usually are discrete features

DIGITAL SPATIAL DATA RASTER VECTOR Real World This is an illustration of transferring a real world geographic area into the raster and vector formats. In the raster format the geographic area is parceled into numerous grid squares and the value that makes up the majority of the square dictates that that entire square gets the value. In the vector format all entities are classified into points, lines, or polygons. Source: Defense Mapping School National Imagery and Mapping Agency

Raster and Vector Data Models Real World 600 1 2 3 4 5 6 7 8 9 10 1 B G Trees 500 2 B G G 3 B 400 4 B G G Trees Y-AXIS 5 B G G 300 6 B G G BK House 7 B 200 8 B B River 9 B 100 10 B 100 200 300 400 500 600 X-AXIS Raster Representation Vector Representation Source: Defense Mapping School National Imagery and Mapping Agency

Example: Discrete raster

Example: continuous raster Xie et al. 2005

Raster Real world Vector Heywood et al. 2006

Effects of changing resolution Heywood et al. 2006

Vector – Advantages and Disadvantages Good representation of reality Compact data structure Topology can be described in a network Accurate graphics Disadvantages Complex data structures Simulation may be difficult Some spatial analysis is difficult or impossible to perform

Raster – Advantages and Disadvantages Simple data structure Easy overlay Various kinds of spatial analysis Uniform size and shape Cheaper technology Disadvantages Large amount of data Less “pretty” Projection transformation is difficult Different scales between layers can be a nightmare May lose information due to generalization

GIS data formats (file formats) Shapefiles Coverages TIN (e.g. elevation can be stored as TIN) Triangulated Irregular Network Grid (e.g. elevation can be stored as Grid) Image (e.g. elevation can be stored as image, all remote sensing images) Vector data Raster data

Shape Files Nontopological Advantages no overhead to process topology Disadvantages polygons are double digitized, no topologic data checking At least 3 files .shp .shx .dbf

Coverages Original ArcInfo Format Directory With Several Files Database Files are stored in the Info Directory Uses Arc Node Topology Containment (coincident) Connectivity Adjacency

Evolution of Vector Data Model ESRI, Inc. Arc/Info: coverages ArcView: shapefiles ArcGIS: geodatabase

Geodatabase components- vector data and table Primary (basic) components - feature classes, - feature datasets, - nonspatial tables. complex components building on the basic components: - topology, - relationship classes, - geometric networks

Geodatabase components- Raster data Raster data referenced only in personal geodatabase Raster data physically stored in multiuser geodatabse Raster datasets and raster catalogs A raster dataset is created from one or more individual rasters. When creating a raster dataset from multiple rasters, the data is mosaicked, or aggregated, into a single, seamless dataset in which areas of overlap have been removed. The input rasters must be contiguous (adjacent) and have the same properties, including the same coordinate system, cell size, and data format. For each raster dataset (.img, grid, JPEG, MrSID, TIFF), ArcGIS creates an ERDAS IMAGINE file (.img). A raster catalog is defined as a table in the geodatabase which you can view like any other table in ArcCatalog. Each raster in the catalog is represented by a row in the table. It contains a collection of rasters that can be noncontiguous, stored in different formats, and have other different properties. In order to view all the rasters in the catalog, they must have the same coordinate system and a common geographic extent

2. Attribute data Attribute data is about “what” of a spatial data and is a list or table of data arranged as rows and columns Rows are records (map features) Each row represents a map feature, which has a unique label ID or object ID Columns are fields (characteristics) Intersection of a column and a row shows the values of attributes, such as color, ownership, magnitude, classification,…

examples

Relational database A relational database is a collection of tables, also called relations, which can be connected to each other by keys. A primary key represents one or more attributes whose values can uniquely identify a record in a table. Its counterpart in another table for the purpose of linkage is called a foreign key Advantages Each table in the database can be prepared, maintained, and edited separately from other tables Efficient data management and processing, since linking tables query and/or analysis is often temporary

Join and relate tables Once tables are separated as relational tables, then two operations can be used to link those tables during query and analysis Join, brings together two tables based on a common key. Relate, connects two tables (based on keys) but keeps the tables separate. Keys do not have to have the same name but must be of the same data type Join relate Join relate

The joined table The joined table will only preserved within the map document-the tables remain separate on disk-and can be removed at any time

Related tables The related table will only preserved within the map document-the tables remain separate on disk-and can be removed at any time

3. metadata Meta is defined as a change or transformation. Data is described as the factual information used as a basis for reasoning. Put these two definitions together and metadata would literally mean "factual information used as a basis for reasoning which describes a change or transformation." In GIS, Metadata is data about the data. It consists of information that describes spatial data and is used to provide documentation for data products. Metadata is the who, what, when, where, why, and how about every facet of the spatial data. According to the Federal Geographic Data Committee (FGDC), metadata is data about the content, quality, condition, and other characteristics of data.

Why use and create metadata To help organize and maintain an organization's spatial data - Employees may come and go but metadata can catalogue the changes and updates made to each spatial data set and how each employee implemented them To provide information to other organizations and clearinghouses to facilitate data sharing and transfer - It makes sense to share existing data sets rather than producing new ones if they are already available To document the history of a spatial data set - Metadata documents what changes have been made to each data set, such as changes in geographic projection, adding or deleting attributes, editing line intersections, or changing file formats. All of these could have an effect on data quality.

Metadata Should Include Data about Date of data collected. Date of coverage generated. Bounding coordinates. Processing steps. Software used RMSE, etc. From where original data came. Who did processing. Projection coordinate System Datum Units Spatial scale Attribute definitions Who to contact for more information Example of non-standard metadata: (source: http://ag.arizona.edu/SRER/index.html) THIS COVERAGE HAS BEEN PROJECTED TO THE FOLLOWING PARAMETERS - September 2000: Projection: UTM Zone: 12 Units: meters Datum: NAD83 (previously NAD27) Spheroid: GRS80 (previously Clarke 1866) THIS DOCUMENT WAS CREATED AT ART, UNIVERSITY OF ARIZONA, September 14, 2001 NAME OF DATASET: QUADS DATA TYPE: Vector-polygon DESCRIPTION OF CONTENT: These are the boundary polygons of the four quadrangles for the Santa Rita Experimental Range in southeast Arizona. FORMAT: Arc/Info DATA SIZE: Approximate Megabytes: .017 HISTORY: This coverage was originally reselected out of the ART Arizona General Reference Library which contains data originally from ALRIS. MAINTENANCE: NONE COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC AREA 4 12 F 3 PERIMETER 4 12 F 3 QUADS# 4 5 B - QUADS-ID 4 5 B - QUAD 4 4 I - 21 NAME 25 25 C – USER ATTRIBUTES QUAD -- the ALRIS 4 digit quad codes NAME -- USGS quad sheet name See an example of non-standard metadata (see)

Federal Geographic Data Committee’s (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM) The FGDC is developing the National Spatial Data Infrastructure (NSDI) in cooperation with organizations from State, local and tribal governments, the academic community, and the private sector. The NSDI encompasses policies, standards, and procedures for organizations to cooperatively produce and share geographic data. The objectives of the CSDGM are to provide a common set of terminology and definitions for the documentation of digital geospatial data.

CSDGM (FGDC-STD-001-1998) Metadata = Identification_Information Data_Quality_Information Spatial_Data_Organization_Information Spatial_Reference_Information Entity_and_Attribute_Information Distribution_Information Metadata_Reference_Information Identification Information – basic information about the data set. Examples include title, geographic area covered, currentness, and rules for acquiring or using the data. Data Quality Information − an assessment of the quality of the data set. Examples include positional and attribute accuracy, completeness, consistency, sources of information, and methods used to produce the data. Spatial Data Organization Information − the mechanism used to represent spatial information in the data set. Examples include the method used to represent spatial positions directly (such as raster or vector) and indirectly (such as street addresses or county codes) and the number of spatial objects in the data set. Spatial Reference Information − description of the reference frame for, and means of encoding, coordinates in the data set. Examples include the name of and parameters for map projections or grid coordinate systems, horizontal and vertical datums, and the coordinate system resolution. Entity and Attribute Information − information about the content of the data set, including the entity types and their attributes and the domains from which attribute values may be assigned. Examples include the names and definitions of features, attributes, and attribute values. Distribution Information − information about obtaining the data set. Examples include contact information for the distributor, available formats, information about how to obtain data sets online or on physical media (such as cartridge tape or CD-ROM), and fees for the data. Metadata Reference Information - information on the correctness of the metadata information, and the responsible party. Connect to http://www.fgdc.gov/metadata/csdgm/

Metadata tools Metadata editors: - tkme / USGS - ArcCatalog / ESRI - SMMS / Intergraph - FGDCMETA / Illinois State Geological Survey - xtme / USGS Metadata utilities (check compliance and export to text, HTML,XML, or SGML): - mp / USGS - MP batch / Intergraph - ArcCatalog powered by mp/ ESRI Metadata Server - Isite / FGDC - GeoConnect Geodata Management Server / Intergraph - ArcIMS Metadata Server / ESRI mp: Metadata Parser

4. Geodatabase Before geodatabase, in one GIS project, many GIS files (spatial data and nonspatial data) are stored separated. So for a large GIS project, the GIS files could be hundreds. Within a geodatabase, all GIS files (spatial data and nonspatial data) in a project can be stored in one geodatabase, using the relational database management system (RDMS)

Types of geodatabases personal enterprise

Personal Geodatabase The personal geodatabase is given a name of filename.mdb that is browsable and editable by the ArcGIS, and it can also be opened with Microsoft Access. It can be read by multiple people at the same time, but edited by only one person at a time. maximum size is 2 GB.

Multiuser Geodatabase Multiuser (ArcSDE or enterprise) geodatabase are stored in IBM DB2, Informix, Oracle, or Microsoft SQL Server. It can be edited through ArcSDE by many users at the same time, is suitable for large workgroups and enterprise GIS implementations. no limit of size. support raster data.

3-tier ArcSDE client/server architecture with both the ArcSDE and Oracle RDBMS running on the same server, which minimizes network traffic and client load while increasing the server load compared to 2-tier system, in which the clients directly connect to the RDBMS

Personal and Multiuser Geodatabase Comparison source: www.esri.com

5. Geometric transformation projection and coordinate system is to project the 3D earth to 2D plane, so the 3D earth can be represented in different GIS data models (2D digital format) in a GIS system. Geometric transformation is the process of using a set of control points and transformation equations to register a (2D) digitized map, a satellite image, or an air photograph onto a (2D) projected coordinate system. In GIS, geometric transformation includes map-to-map transformation, image-to-map transformation, image-to-image transformation. The root mean square (RMS) error is a quantitative measure of location accuracy that can determine the quality of a geometric transformation. Image to map (or image) needs an additional step resampling to fill the cell values from the original image.

6. Data accuracy and quality Raster data quality Geolocation accuracy Estimation accuracy Vector data quality Location errors Topographical errors Location and value

7. Vector data analysis Vector data analysis uses the spatial features of point, line, and polygon as inputs. The accuracy of analysis results depends on the accuracy of spatial features in terms of location and shape. Topology can also be a factor for some vector data analyses such as buffering and overlay. Pattern Analysis Intersect Union Symmetrical difference Identity AND OR XOR AND OR Point pattern: nearest neighbor, Ripley’s K-function Moran’s I G-Statistic

8. Raster data analysis Raster data analysis is based on cells and rasters. Raster data analysis can be performed at the level of individual cells, or groups of cells, or cells within an entire raster. Some raster data operations use a single raster, while others use two or more rasters. Raster data analysis is also related to the type of cell value (numeric or categorical values) in the input raster(s). Local, focal, zonal Allocation and direction Clip and mosaic Aggregate and regiongroup Map algebra

9. Lab 1 Getting Started With the Geodatabase

COPY the result map of your last step to your home work

Copy your exam questions and result to your homework