Elements of geographic information
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There are some universal principles that provide the foundation for how GIS systems represent, operate on, manage, and share geographic information. The purpose of this topic is to provide you with a solid foundation for understanding these key concepts and how ArcGIS employs them.

Like a map, a GIS is layer-based. And like the layers in a map, GIS datasets represent collections of individual features with their geographic locations and shapes as well as with descriptive information stored as attributes.

There are four fundamental types of geographic representations:

• Features (collections or points, lines, and polygons)
  
• Attributes
  
• Imagery
  
• Continuous surfaces (such as elevation)
  

All of the rich GIS behavior for representing and managing geographic information is based on these fundamental types.

Features - Points, lines, and polygons

Geographic features are representations of things located on or near the surface of the earth. Geographic features can occur naturally (such as rivers and vegetation), can be constructions (such as roads, pipelines, wells, and buildings), and can be subdivisions of land (such as counties, political divisions, and land parcels).

Although there are a number of additional types, geographic features are most commonly represented as points, lines, and polygons.

Points define discrete locations of geographic features too small to be depicted as lines or areas, such as well locations, telephone poles, and stream gauges. Points can also represent locations such as address locations, GPS coordinates, or mountain peaks.



Lines represent the shape and location of geographic objects too narrow to depict as areas (such as street centerlines and streams). Lines are also used to represent features that have length but no area such as contour lines and administrative boundaries. (Contours are interesting, as you¨ll read later on, because they provide one of a number of alternatives for representing continuous surfaces.)



Polygons are enclosed areas (many-sided figures) that represent the shape and location of homogeneous features such as states, counties, parcels, soil types, and land use zones. In the example below, the polygons represent Parcels.

Attributes

Maps convey descriptive information through map symbols, colors, and labels. Here are some typical examples:

• Roads are displayed based on their road class (for example, line symbols representing divided highways, main streets, residential streets, unpaved roads, and trails).
  
• Streams and water bodies are drawn in blue to indicate water.
  
• City streets are labeled with their name and often some address range information.
  
• Special point and line symbols denote specific features such as rail lines, airports, schools, hospitals, and special facilities.
  

In a GIS, descriptive attributes are managed in tables, which are based on a series of simple, essential relational database concepts. A relational database provides a simple, universal data model for storing and working with attribute information. DBMSs are inherently open because their simplicity and flexibility enables support for a broad range of applications. Key relational concepts include:

• Descriptive data is organized into tables.
  
• Tables contain rows.
  
• All rows in a table have the same columns.
  
• Each column has a type, such as integer, decimal number, character, date, and so on.
  
• A series of relational functions and operators (SQL) is available to operate on the tables and their data elements.
  

The illustration below shows two tables and how their records can be related to one another using a common field. In the example, the parcels feature class table is linked to the owners table through the common Property ID field.

Imagery

Aerial imagery is a raster data structure obtained from various sensors carried in satellites and aircraft. Imagery is managed as a raster data type composed of cells organized in a grid of rows and columns. In addition to the map projection, the coordinate system for a raster dataset includes its cell size and a reference coordinate (usually the upper left or lower left corner of the grid).

These properties enable a raster dataset to be described by a series of cell values starting in the upper left row. Each cell location can be automatically located using the reference coordinate, the cell size, and the number of rows and columns.



Typical image sources include cameras capable of capturing aerial photographs that can be georeferenced and corrected to ground locations (such as digital ortho photography).



Imagery is also used to collect data in both the visible and non-visible portions of the electromagnetic spectrum. One system is the multispectral scanner carried in LANDSAT satellites that records imagery in seven bands (or ranges) along the electromagnetic spectrum. The measures for each band are recorded in a separate grid. The stack of seven grids makes up a multiband image.

Surfaces

A surface describes an occurrence that has a value for every point on the earth. For example, surface elevation is a continuous layer of values for ground elevation above mean sea level for the entire extent of the dataset. Other surface type examples include rainfall, pollution concentration, and sub-surface representations of geological formations.

Surface representation is somewhat challenging. With continuous datasets, it is impossible to represent all values for all locations. Various alternatives exist for representing surfaces using either features or rasters. Here are some example alternatives for surface representation:

Contour lines—Isolines represent locations having an equal value, such as elevation contours.



Contour bands—The areas where the surface value is within a specified range, such as bands of average annual rainfall between 25 CM and 50 CM per year.



Raster datasets—A matrix of cells where each cell value represents a measure of the continuous variable. For example, Digital Elevation Models (DEMs) are frequently used to represent surface elevation.



TIN layers—A Triangulated Irregular Network (TIN) is a data structure for representing surfaces as a connected network of triangles. Each triangle node has an XY coordinate and a Z or surface value.



The raster and TIN representations can be used to estimate the surface value for any location using interpolation.

Georeferencing and coordinate systems
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Georeferencing: Assigning map coordinates and spatial location

All the elements in a map layer have a specific geographic location and extent that enables them to be located on or near the earth¨s surface. The ability to accurately describe geographic locations is critical in both mapping and GIS. This process is called georeferencing.



Describing the correct location and shape of features requires a framework for defining real-world locations. A geographic coordinate system is used to assign geographic locations to objects. A global coordinate system of latitude-longitude is one such framework. Another is a planar or Cartesian coordinate system derived from the global framework.

Maps represent locations on the earth¨s surface using grids, graticules, and tic marks labeled with various ground locations (both in measures of latitude-longitude and in projected coordinate systems (such as UTM meters). The geographic elements contained in various map layers are drawn in a specific order (on top of one another) for the given map extent.

GIS datasets contain coordinate locations within a global or Cartesian coordinate system to record geographic locations and shapes.

Latitude and longitude

One method for describing the position of a geographic location on the earth¨s surface is using spherical measures of latitude and longitude. They are measures of the angles (in degrees) from the center of the earth to a point on the earth¨s surface. This reference system is often referred to as a geographic coordinate system.



Latitude angles are measured in a north-south direction. The equator is at an angle of 0. Often, the northern hemisphere has positive measures of latitude and the southern hemisphere has negative measures of latitude. Longitude measures angles in an east-west direction. Longitude measures are traditionally based on the Prime Meridian, which is an imaginary line running from the North Pole through Greenwich, England to the South Pole. This angle is Longitude 0. West of the Prime Meridian is often recorded as negative Longitude and east is recorded as positive. For example, the location of Los Angeles, California is roughly Latitude "plus 33 degrees, 56 minutes" and Longitude "minus 118 degrees, 24 minutes."



Although longitude and latitude can locate exact positions on the surface of the globe, they are not uniform units of measure. Only along the equator does the distance represented by one degree of longitude approximate the distance represented by one degree of latitude. This is because the equator is the only parallel as large as a meridian. (Circles with the same radius as the spherical earth are called great circles. The equator and all meridians are great circles.)

Above and below the equator, the circles defining the parallels of latitude get gradually smaller until they become a single point at the North and South Poles where the meridians converge. As the meridians converge toward the poles, the distance represented by one degree of longitude decreases to zero. On the Clarke 1866 spheroid, one degree of longitude at the equator equals 111.321 km, while at 60  latitude, it is only 55.802 km. Since degrees of latitude and longitude don¨t have a standard length, you can¨t measure distances or areas accurately or display the data easily on a flat map or computer screen. Performing GIS analysis and mapping applications requires a more stable coordinate framework, which is provided by projected coordinate systems.

Map projections using Cartesian coordinates

Projected coordinate systems are any coordinate system designed for a flat surface, such as a printed map or a computer screen.

2D and 3D Cartesian coordinate systems provide the mechanism for describing the geographic location and shape of features using x and y values (and, as you will read later, by using columns and rows in rasters).

The Cartesian coordinate system uses two axes: one horizontal (x), representing east-west, and one vertical (y), representing north-south. The point at which the axes intersect is called the origin. Locations of geographic objects are defined relative to the origin, using the notation (x,y), where x refers to the distance along the horizontal axis, and y refers to the distance along the vertical axis. The origin is defined as (0,0).

In the illustration below, the notation (4, 3) records a point that is four units over in x and three units up in y from the origin.

3D coordinate systems

Increasingly, projected coordinate systems also use a Z value to measure elevation above or below mean sea level.

In the illustration below, the notation (2, 3, 4) records a point that is two units over in x and three units in y from the origin and whose elevation is 4 units above the earth¨s surface (such as 4 meters above mean sea level).

Properties and distortion in map projections

Since the earth is spherical, a challenge faced by cartographers and GIS professionals is how to represent the real world using a flat or planar coordinate system. To understand their dilemma, consider how you would flatten half of a basketball; it can¨t be done without distorting its shape or creating areas of discontinuity. The process of flattening the earth is called projection, hence the term map projection.



A projected coordinate system is defined on a flat, two dimensional surface. Projected coordinates can be defined for both 2D (x,y) and 3D (x,y,z) in which the x,y measurements represent the location on the earth¨s surface and z would represent height above or below mean sea level.

Below are some examples of various methods for deriving planar map projections.





Unlike a geographic coordinate system, a projected coordinate system has constant lengths, angles, and areas across the two dimensions. However, all map projections representing the earth¨s surface as a flat map, create distortions in some aspect of distance, area, shape, or direction.

Users cope with these limitations by using map projections that fit their intended uses, geographic location, and extent. GIS software also can transform information between coordinate systems to support integration and critical workflows.

Many map projections are designed for specific purposes. One map projection might be used for preserving shape while another might be used for preserving the area (conformal versus equal area).

These properties—the map projection (along with Spheroid and Datum), become important parameters in the definition of the coordinate system for each GIS dataset and each map. By recording detailed descriptions of these properties for each GIS dataset, computers can re-project and transform the geographic locations of dataset elements on the fly into any appropriate coordinate system. As a result, it¨s possible to integrate and combine information from multiple GIS layers. This is a fundamental GIS capability. Accurate location forms the basis for almost all GIS operations.

Learn more about Map Projections

How ArcGIS users work with geographic information
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Users work with geographic data in two fundamental ways:

• As datasets, which are homogeneous collections of features, rasters, or attributes—such as parcels, wells, buildings, orthophoto imagery, and raster-based digital elevation models (DEMs)
  


• As individual elements—such as individual features, rasters, and attribute values—contained within each dataset
  

In a GIS, homogeneous collections of geographic objects are organized into datasets about common subjects, such as parcels, wells, roads, buildings, orthophoto imagery, and raster-based digital elevation models (DEMs).

Users work with GIS datasets

Geographic datasets are the primary object collections that users work with in a GIS. They also represent the most common method for data sharing among GIS users.

Datasets are used as the basis for most GIS operations, and provide the primary data sources for:

• Maps
  
• Globes and 3D scenes
  
• Geoprocessing inputs and derived datasets
  
• Data sharing with other users
  

Datasets provide the key inputs for mapping and visualization. Each layer in a map references a dataset and specifies how it will be drawn and labeled.



The map display above was created by drawing numerous datasets—feature classes of cities, country boundaries, rivers, and water bodies—on top of a raster dataset of shaded relief.

Datasets are also used as sources for layers in ArcGlobe and ArcScene views.



Datasets are the primary inputs and outputs for geoprocessing. ArcGIS includes a rich set of geoprocessing tools. Each tool takes datasets as inputs, performs a transformation on these datasets, and creates results—called derived datasets. A sequence of operations can be assembled into a process to automate workflows, do analysis, and to automate many critical GIS tasks—hence the term geoprocessing.



Datasets are the primary means for data sharing. GIS users primarily share their information as individual datasets. Datasets typically come in any number of formats: CAD files, image files, tables, shapefiles, GML files, and so on. A key goal of ArcGIS is for users to work with all the commonly external file formats as well as ESRI supported formats, such as the geodatabase.

Datasets can be listed in ArcCatalog and can be copied and distributed to other GIS users:


ArcGIS supports datasets in its native geodatabase as well as multiple GIS file formats. ArcGIS works with geographic datasets that are managed in geodatabases as well as in numerous GIS file formats. Geodatabase datasets represent the native data structure for ArcGIS and are the primary data format used for editing and data management.

Users work with individual data elements held in each dataset

In addition to working with datasets, users also work with the individual elements contained in datasets. These elements include individual features, rows and columns in attribute tables, and individual cells in raster datasets. For example:

When you identify a parcel by pointing at it, you¨re working with the individual data elements in a dataset:



You work with individual data elements when you edit features, a road centerline in this case:



In tables, users work with descriptive information contained in rows and columns.

How GIS represents and organizes geographic information
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Managing features, rasters, attributes, and surfaces in ArcGIS

These four types of geographic information (features, rasters, attributes, and surfaces) are actually managed using three primary GIS data structures:

• Feature classes
  
• Attribute tables
  
• Raster datasets
  

Each of these primary datasets can be extended with additional capabilities to maintain data integrity (for example, using topology), to model geographic relationships (such as network connectivity and flow), and to add advanced behaviors (for example, using TINs).

How the four types of map layers are represented in GIS

Map Layer Types	GIS Datasets
Features—points, lines, and polygons	Feature classes
Attributes	Tables
Imagery	Raster datasets
Surfaces	Both features and rasters can be used to provide a number of alternative surface representations: Feature classes (such as contours) Raster-based elevation datasets TINs built from XYZ points and 3D line feature classes

A GIS has a collection of datasets

Typically, a GIS is used for handling several different datasets where each holds data about a particular feature collection (for example, roads) that is geographically referenced to the earth¨s surface.

A GIS database design is based upon a series of data themes, each having a specified geographic representation. For example, individual geographic entities can be represented as features (such as points, lines, and polygons); as imagery using rasters; as surfaces using features, rasters, or TINs; and as descriptive attributes.

In a GIS, homogeneous collections of geographic objects are organized into data themes such as parcels, wells, buildings, ortho imagery, and raster-based digital elevation models (DEMs). Precisely and simply defined geographic datasets are critical for useful geographic information systems, and the layer-based concept of data themes is a critical GIS concept.

GIS datasets are collections of geographic representations

A dataset is a collection of homogeneous features. Geographic representations are organized in a series of datasets or layers. Most datasets are collections of simple geographic elements such as a road network, a collection of parcel boundaries, soil types, an elevation surface, satellite imagery for a certain date, well locations, and so on.

In a GIS, spatial data collections are typically organized as feature class datasets or raster-based datasets.

Many data themes are best represented by a single dataset such as for soil types or well locations. Other themes, such as a transportation framework, are represented by multiple datasets (such as a separate feature class each for streets, intersections, bridges, highway ramps, railroads, and so on).

Raster datasets are used to represent georeferenced imagery as well as continuous surfaces such as elevation, slope, and aspect.

Common GIS representations

Theme	Geographic representation
Hydrography	Lines
Road centerlines	Lines
Vegetation	Polygons
Urban areas	Polygons
Administrative boundaries	Polygons
Elevation contours	Lines
Well locations	Points
Orthophotography	Raster
Satellite imagery	Raster
Land parcels	Polygons
Parcel tax records	Tables

Datasets are the organizing principle in a GIS database

The concept of a data theme was one of the early notions in GIS. Historically, GIS practitioners thought about how the geographic information in maps could be partitioned into a series of logical information layers—as more than a random collection of objects. They envisioned homogeneous collections of representations that could be managed as layers and that these data layers could be combined through georeferencing. These early GIS users organized information in various data themes that described the distribution of a phenomenon and how each should be portrayed across a geographic extent.

These layers also provided a protocol for collecting the representations. For example, a data theme could be defined that delineated various areas representing the dominant soil type (that is, a layer collection of soil type polygons). Each and every area (the polygons) in a specified extent could be assigned an explicit soil type, and the soil types could be described using properties or attributes of each polygon.



Each GIS will contain multiple themes for a common geographic area. The collection of themes acts as a stack of layers. Each theme can be managed as an information set independent of other themes. Each has its own representation (as a collection of points, lines, polygons, surfaces, rasters, and so on). Because layers are spatially referenced, they overlay one another and can be combined in a common map display. GIS analysis operations, such as polygon overlay, can fuse information between data layers to discover and work with the derived spatial relationships.

Implications of layer-based data themes

The GIS design concept of layer-based data themes has some key implications:

• All layers must be georeferenced to a place on the earth. This is accomplished by defining the geographic coordinate system for each dataset.
  
• Layers can be combined in many ways—some that are visual (such as the ordered layers in a map)—and some that employ tool-based operators or "commands". Geographic operators can be used to identify and work with the relationships both within as well as between layers. One key aspect of GIS is that it provides a sophisticated series of operators that work against the georeferenced collection of data layers. GIS users often refer to this as geoprocessing.
  
• GIS layers can be combined from many sources and many users. In fact, most users are dependent on one another for portions of the data they want to use. Interoperability is fundamental in GIS.
  

GIS uses many datasets and data types

A GIS will use numerous datasets, each containing its specific representation, often from many organizations. A number of alternative file formats and schemas will be used across a range of systems, but users still have the need to share and re-use each other¨s data.

Therefore, it is important for GIS datasets to be:

• Simple to use and easy to understand
  
• Combined easily with other geographic datasets (for example, each has a well-defined spatial reference)
  
• Effectively compiled and validated
  
• Clearly documented for content, intended uses, and purposes
  

Any effective GIS database or file base will adhere to these common principles and concepts regardless of its format. Each GIS requires a mechanism for describing geographic data in these terms along with a comprehensive set of tools to use and manage this information.

How maps convey geographic information
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Fundamental GIS concepts are closely linked to maps and their contents. In fact, map concepts form the basis for understanding GIS more fully. This topic explores some fundamental map concepts and describes how they are applied and used within GIS.

Maps

A map is a collection of map elements laid out and organized on a page. Common map elements include the map frame with map layers, a scale bar, north arrow, title, descriptive text, and a symbol legend.

The primary map element is the map frame, and it provides the principal display of geographic information. Within the map frame, geographical entities are presented as a series of map layers that cover a given map extent—for example, map layers such as roads, rivers, place names, buildings, political boundaries, surface elevation, and satellite imagery.

The following graphic illustrates how geographical elements are portrayed in maps through a series of map layers. Map symbols and text are used to describe the individual geographic elements.



Map layers are thematic representations of geographic information, such as transportation, water, and elevation. Map layers help convey information through:

• Discrete features such as collections of points, lines, and polygons
  
• Map symbols, colors, and labels that help to describe the objects in the map
  
• Aerial photography or satellite imagery that covers the map extent
  
• Continuous surfaces such as elevation which can be represented in a number of ways—for example, as a collection of contour lines and elevation points or as shaded relief
  

Map Layout and composition

Along with the map frame, a map presents an integrated series of map elements laid out and arranged on a page. Common map elements include a north arrow, a scale bar, a symbol legend, and other graphical elements. These elements aid in map reading and interpretation.

The map layout below illustrates how map elements are arranged on a page.



Often, maps include additional elements such as graphs, charts, pictures, and text that help to communicate additional critical information.

Spatial relationships in a map

Maps help convey geographic relationships that can be interpreted and analyzed by map readers. Relationships that are based on location are referred to as spatial relationships. Here are some examples.

• Which geographic features connect to others (for example, Water Street connects with 18th Ave.)
  


• Which geographic features are adjacent (contiguous) to others (for example, The city park is adjacent to the university.)
  


• Which geographic features are contained within an area (for example, The building footprints are contained within the parcel boundary.)
  


• Which geographic features overlap (for example, The railway crosses the freeway.)
  


• Which geographic features are near others (proximity) (for example, The Courthouse is near the State Capitol.)
  


• The feature geometry is equal to another feature (for example, The city park is equal to the historic site polygon).
  


• The difference in elevation of geographic features (for example, The State Capitol is uphill from the water.)
  


• The feature is along another feature (for example, The bus route follows along the street network.).
  

Within a map, such relationships are not explicitly represented. Instead, as the map reader, you interpret relationships and derive information from the relative position and shape of the map elements, such as the streets, contours, buildings, lakes, railways, and other features. In a GIS, such relationships can be modeled by applying rich data types and behaviors (for example, topologies and networks) and by applying a comprehensive set of spatial operators to the geographic objects (such as buffer and polygon overlay).

Users work with many data types and data formats in ArcGIS
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Users work with many data types and formats in ArcGIS

ArcGIS supports geographic datasets that are managed in geodatabases as well as in numerous GIS file formats. Geodatabase datasets represent the native data structure for ArcGIS and are the primary data format used for editing and data management. Yet, many additional datasets can be used.

A number of additional file formats are supported. These can be used in ArcGIS much like geodatabase datasets—to create layers in ArcMap and ArcGlobe; as inputs for Geoprocessing operations; to be viewed and queried in charts, maps, globes, and tables; and converted to and from many other GIS formats.

The following table lists some of the dataset file types commonly used in ArcGIS.

Some commonly used external data files in ArcGIS

ESRI	Coverage	ArcInfo Workstation coverages
	Grid	ArcInfo GRID raster format
	Tin	ArcInfo triangulated irregular network (TIN) format
	Shapefile (SHP)	ESRI shapefile format
Vector	TIGER/Line	U.S. Census Bureau¨s TIGER/Line Files
	MIF/MID	MapInfo Vector Interchange File MapInfo Table Interchange for MIF
	TAB	MapInfo Native Dataset
	VPF	National Geospatial Intelligence Agency¨s Vector Product File format
	GML	Open Geospatial Consortium¨s GML Interchange Specification
Raster	IMG	Leica ERDAS Imagine image files
	BMP	Bitmap raster format
	TIF	TIFF raster format
	JPG	JPEG raster compression format
	JP2	JPEG 2000 raster format
	SID	MrSID raster format
CAD	DXF	CAD transfer file. Uses ASCII or binary drawing file interchange.
	DGN	MicroStation design file format
	DWG	AutoCAD drawing file format
Tables	XLS	Excel spreadsheets
	DBF	dBase data file format
	Info	Arc/Info Workstation INFO tables
	MDB	File format for Microsoft¨s Access database
	TXT	Text file often used to hold attribute columns delimited by commas or tabs

In addition to these file and RDBMS data sources, ArcGIS can work with numerous additional formats through data conversion. GIS data can also be accessed through networks using Web services and various XML schemas. XML support includes, among others, ArcXML, SOAP, the Open Geospatial Consortium¨s WMS and WFS protocols, and Geodatabase XML.

See Data support in ArcGIS for more information.

The ArcGIS Data Interoperability extension

The ArcGIS Data Interoperability extension provides direct read access to dozens of additional spatial data formats not already supported in ArcGIS. For example, you can use the Data Interoperability extension to add support for various GML profiles as well as advanced data formats in DWG/DXF, MicroStation Design, MapInfo MID/MIF, and TAB file types.

You can convert to and from these data types and geodatabases using this extension. More importantly, you can use Data Interoperability to directly use these formats in ArcGIS. Users can drag and drop these and many other external data sources into ArcGIS for general use in mapping, geoprocessing, metadata management, and 3D globe use. For example, you can make use of all the mapping functions available to native ESRI formats inside ArcMap for these data sources—such as viewing features and attributes, identifying features, and making selections.

The ArcGIS Data Interoperability extension is developed and maintained collaboratively by ESRI and Safe Software Inc., the leading GIS interoperability vendor, and is based on Safe Software’s popular Feature Manipulation Engine (FME) product.

The ArcGIS Data Interoperability extension also includes FME Workbench, which contains a series of data transformation tools to build converters for many complex vector data formats.

Learn more about Data Interoperability Extension