Geodatabase PDF

Geodatabase Data Management toolbox The Data Management toolbox provides a rich and varied collection of tools that are used to develop, manage, and maintain feature classes, datasets, layers, and raster data structures. While the Analysis toolbox is used to solve spatial or statistical que...

Geodatabase Data Management toolbox The Data Management toolbox provides a rich and varied collection of tools that are used to develop, manage, and maintain feature classes, datasets, layers, and raster data structures. While the Analysis toolbox is used to solve spatial or statistical questions and the Conversion toolbox is necessary for the conversion of various data formats, the Data Management toolbox lets you perform functions from simple tasks like managing basic structures, such as fields and workspaces, to more complex tasks related to topology and versioning. Spatial Data Format Geographic data formats are based on representations (models) of the real world that can be used in a GIS to produce maps, perform interactive queries, and execute analyses. The Main spatial data format can be: ❖ CAD file ❖ Shapefile ❖ Coverage ❖ Geodatabase ❖ Simple feature CAD Drawings CAD (computer-aided design) drawing files is binary file format with little attribute information. A characteristic of CAD files is that features are typically subdivided into many layers. “Layer” in a CAD file has a different meaning than “layer” in a map. In a CAD file, it represents a set of similar features. In a map, it represents a reference to a geographic dataset or feature class with an associated drawing method. A CAD dataset is the catalog’s representation of CAD drawing files. It is subdivided into CAD feature classes, each of which aggregates all of the layers for points, lines, polygons, or annotation. Shapefile It based on georelational model and topology is not maintained. A shapefile is composed of three main files that contain spatial and attribute data. A shapefile can optionally have other files with index information. In the catalog, all these files that comprise a shapefile appear as one feature class. A shapefile is a homogeneous collection of features that can have either point, multipoint, polyline, or polygon shapes. A point shapefile contains features with point geometry. A point is a single coordinate value. Shapefiles used a very simple storage model for feature coordinates. Shapefiles could be easily created from coverages as well as many other geographic information systems.  A multipoint shapefile contains features with multipoint geometries, in which several points represent one feature. A line shapefile contains features with polyline geometry. Polylines are made of paths, which are simply connected sets of line segments. The paths in a polyline can be connected, disjoint, or intersecting. A polygon shapefile contains features with polygon geometry. A polygon contains one or many rings. A ring is a closed path that cannot intersect itself. The rings in a polygon can be disjoint, nested, or intersect one another. While shapefiles store attributes in an embedded dBASE file, attributes of other objects can be stored in another dBASE table and can be joined to a shapefile by an attribute key. Coverage Coverages contain feature classes that are homogeneous collections of features and it is based on georelational model. The primary types of coverage features are points, arcs (lines), polygons, and nodes. These features have topological associations: arcs form the perimeter of polygons, nodes form the endpoints of arcs, points mark the interiors of polygons. Point features have a dual identity; they can represent small geographic objects such as wells and buildings and they can mark polygon interiors. Secondary types of coverage features are tics, links, and annotation. Tics are used for map registration, links are used for adjusting features, and annotation is used to label features on a map.  Coverages also contain composite features. Routes are collections of arcs with an associated measurement system. A common use of routes is for transportation systems. Regions are collections of polygons that can be adjacent, disjoint, or overlapping. Regions are used for land-use and environmental applications. Workspace ArcInfo workspaces contain the three basic representations of geographic data— coverages contain vector data, grids contain raster data, and TINs contain triangulations that represent surfaces. Most data stored in a workspace implements the georelational model where topology is stored and attributes are linked to features. An ArcInfo workspace is a special type of folder where attributes for data are stored in INFO tables and all of the tables are managed through an INFO subfolder that is invisible in the catalog. When you use the catalog to create, move, and delete items in an ArcInfo workspace, their integrity is maintained for you. Geodatabase A geodatabase is the top-level unit of geographic data. It is a collection of datasets, feature classes, object classes, and relationship classes. Object-oriented model – can characterize features more naturally by defining object types, topological, spatial and general relationships, and interactions. Create custom features in addition to points, lines, polygons Brings physical model closer to logical model. Geodatabases manage seamless geographic data. There is no partitioning of a geographic area into tiled units. Rather, geodatabases use effective spatial indexing for continuous representation of an extent. Concepts of Geodatabse (GDB) Geodatabases organize geographic data into a hierarchy of data objects. These data objects are stored in feature classes, object classes, and feature datasets. GDB support four spatial data types Vector data for representing features. Raster data for images, grids, and surfaces. Triangulated irregular networks (TINS) for surfaces. Tabular data. ✓ Locators and addresses for finding a geographic position from an address.  Based on Object-Oriented Model. Users can add behavior, properties, rules and relationships to data. Implemented as extension to standard relational database technology. Supports topologically integrated feature classes. Extends the coverage model with support for complex networks, relationships among feature classes, and other object-oriented features Provides platform for development of custom data models using visual tools like CASE (Computer Aided Software Engineering) tools and UML (Unified Modeling Language) notation. Types of GDB Personal (Single-user) Implemented as a Microsoft Access database (*.mdb file) by using MS jet engine). Can be placed on local or network drives. Generally used for personal or small workgroup use. Personal geodatabases can represent small- to medium-sized datasets. Personal geodatabase can yield decent performance for datasets of 250,000 objects or less, maximum size is 2.0 GB. If a personal geodatabase is deleted its gone. Mainly single user editing on small datasets and multiple readers File GDB Stored in a file system Has no theoretical size limit. A table can store up to 256 TB of data using a configuration keyword. Can have more than one concurrent editor (provided they are editing in different tables, feature classes, or feature datasets). Mainly single user editing small to very large datasets Multiple readers ArcSDE GDB (multi-user) ArcSDE is the multiuser data access extension to ArcInfo (bundled w/software) that serves geodatabases to AI applications running on pc’s on TCP/IP network. Used for demanding datasets requiring concurrent editing by multiple users. Very large datasets can be efficiently handled with an enterprise ArcSDE implementation. Created by installing a DBMS and ArcSDE on a server. Can be deployed on UNIX or Windows NT. ArcSDE is centrally tuned and managed by a DBA. Can build SQL applications to access tables in a remote geodatabase. Elements of GDB Objects & Object classes Features & Feature classes Feature datasets Spatial references Domains Subtypes Relationships & Relationship classes Geometric networks Labels and Annotation Objects & Object Classes Geodatabases organize geographic data into a hierarchy of data objects. Objects are instances of an object class that have properties and behavior. Objects can be related to other objects via relationships Objects have unique system identifiers (OID) Object classes are tables in a geodatabase storing nonspaital data (e.g., Parcel owners) Objects in an object class have the same: ✓ Properties stored in the table as attributes ✓ Behavior implemented as a component Feature class A feature class is a collection of features with the same type of geometry: point, line, or polygon and attributes. A feature class is also an object class which stores spatial objects (features) (e.g., Parcels). All the features in a feature class are in the same spatial reference. Feature classes can be: simple and topological. Feature classes which store topological features must be contained within a feature dataset to ensure a common spatial reference.  Simple feature classes contain points, lines, polygons, or annotation without any topological associations among them. That is, points in one feature class may be coincident with, but distinct from, the endpoints of lines in another feature class. These features can be edited independently of each other. Topological feature classes are bound within a graph, which is an object that binds a set of feature classes that comprise an integrated topological unit. Feature datasets Containers for feature classes Shared spatial reference Analogous to a coverage less restrictive May also contain ❖ relationship classes ❖ geometric networks ❖ Annotations  In GDB, geographic datasets can be: the feature dataset, the raster dataset, and the TIN dataset. A feature dataset is a collection of feature classes that share a common coordinate system. It can organize simple feature classes inside or outside of feature datasets, but topological feature classes must be contained within a feature dataset to ensure a common coordinate system. A raster dataset can either be a simple dataset or a compound dataset with multiple bands for distinct spectral or categorical values. A TIN dataset contains a set of triangles that exactly span an area with a z value for each node that represents some type of surface. Spatial reference A spatial reference is a series of parameters that define the coordinate system and other spatial properties for each dataset in the geodatabase. It is typical that all datasets for the same area (and in the same geodatabase) use a common spatial reference definition. A spatial reference includes the following: ❖ The coordinate system ❖ The coordinate precision with which coordinates are stored (often referred to as the coordinate resolution) ❖ Processing tolerances (such as the cluster tolerance) ❖ The spatial extent covered by the dataset (often referred to as the spatial domain) Spatial reference A spatial reference is the coordinate system used to store each feature class and raster dataset as well as other coordinate properties such as the coordinate resolution for x,y coordinates and optional z- and m- (measure) coordinates. a vertical coordinate system can be defined for datasets with z-coordinates that represent surface elevation. GDB schema The geodatabase schema includes the definitions, integrity rules, and behavior for each of these extended capabilities. These include properties for coordinate systems, coordinate resolution, feature classes, topologies, networks, raster catalogs, relationships, domains, and so forth. This schema information is persisted in a collection of geodatabase meta tables in the DBMS. These tables define the integrity and behavior of the geographic information. GIS Data in a GDB The geodatabase is a more robust and extendable data model compared to shapefiles and coverages. It is designed to make full use of the capabilities of ArcGIS for Desktop and ArcGIS for Server. The geodatabase supports all the different elements of GIS data used by ArcGIS. Attribute data Geographic features Satellite and aerial images (raster data) CAD data Surface modelling or 3D data Utility and transportation systems GPS coordinates Survey measurements GIS Data in a GDB Geodatabases can represent these types of data as the following data objects: Annotation A specialized feature class that stores text or graphics that provide information about features or general areas of a map. An annotation feature class may be linked to another feature class so that edits to the features are reflected in the corresponding annotation (i.e., feature-linked annotation). Dimension A special type of geodatabase annotation that shows specific lengths or distances on a map. A dimension feature may indicate the length of a side of a building or land parcel, or it may indicate the distance between two features such as a fire hydrant and the corner of a building. Feature Class A collection of geographic features with the same geometry type (i.e., point, line, or polygon), the same attributes, and the same spatial reference. Feature classes allow homogeneous features to be grouped into a single unit for data storage purposes, for example, highways, primary roads, and secondary roads can be grouped into a line feature class named "roads." Feature classes can also store annotation and dimensions. Feature Dataset A collection of feature classes stored together that share the same spatial reference. Feature classes in a feature dataset share a coordinate system, and their features fall within a common geographic area. Feature datasets are used to help model spatial relationships between feature classes. Geometric Network Edge and junction features that represent a directed-flow system network, such as a utility or hydrologic system, in which the connectivity of features is based on their geometric coincidence. A geometric network does not contain information about the connectivity of features; this information is stored within a logical network. Geometric networks are typically used to model directed-flow systems. Locator A dataset that manages address information for features to enable geocoding, which is a process to transform addresses to a geographic location to display on a map. Mosaic Dataset A new data model within the geodatabase that enables collections of images and rasters to be stored as a catalogue with the option to associate metadata, dynamic mosiacking, and on-the-fly image processing. It is accessible as a raster dataset (with all required processing done on the fly) or as a catalogue of footprints and metadata. Network Dataset A collection of topologically connected network elements (e.g., edges, junctions, and turns) that are derived from network sources, typically used to represent an undirected-flow system network such as a road or subway system. Each network element is associated with a collection of network attributes. Network datasets are typically used to model undirected-flow systems. Parcel Fabric A dataset for the storage, maintenance, and editing of parcels. It is a continuous surface of connected polygon features, line features, and point features. Parcel Fabric replaces the Cadastral Fabric and Survey Dataset. Raster Catalog A collection of raster datasets defined in a table of any format, in which the records define the individual raster datasets that are included in the catalogue. Raster catalogues can be used to display adjacent or overlapping raster datasets without having to mosaic them together in one large file. Raster Dataset Any valid raster format organized into one or more bands. Each band consists of an array of pixels (cells), and each pixel has a value (e.g., a Landsat satellite image). Raster datasets can be stored in many formats, including TIFF, ERDAS Imagine, Esri Grid, and MrSID. Relationship Class A class similar to relationships that exist within an RDBMS. Relationship classes manage the associations between objects in one class (e.g., table or feature class) and objects in another. Objects at either end of the relationship can be features with geometry or records in a table. Schematic Dataset A dataset used for graphically representing network connectivity. It also represents sets of relationships. Table A set of data elements arranged in rows and columns. Each row represents a single record. Each column represents a field of the record. Rows and columns intersect to form cells, which contain a specific value for one field in a record. Tables typically store stand-alone attribute information or information associated with a spatial location such as addresses. Terrain A triangulated irregular network (TIN)-based dataset that uses feature classes as data sources to model multiple resolution surfaces using z-values. Toolbox A collection of dataflow and workflow processes. These are used for performing data management, analysis, and modelling. Topology The arrangement that constrains how point, line, and polygon features share geometry within a geodatabase. For example, street centerlines and census blocks share geometry, and adjacent soil polygons share geometry. Topology defines and enforces data integrity rules, topological relationship queries and navigation, and sophisticated editing tools. It also allows feature construction from unstructured geometry. Why use the Geodatabase? One centralized location for all of your geographic data Model real work advanced spatial relationships Smarter datasets with intelligence Scalable (size and number of users) Better maps Simple The model to best support the ArcGIS platform within an organization  Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships. ArcGIS implements topology through a set of rules that define how features may share a geographic space and a set of editing tools that work with features that share geometry in an integrated fashion. A topology is stored in a geodatabase as one or more relationships that define how the features in one or more feature classes share geometry. The features participating in a topology are still simple feature classes—rather than modifying the definition of the feature class, a topology serves as a description of how the features can be spatially related. Why topology? Topology has long been a key GIS requirement for data management and integrity. A topological data model manages spatial relationships by representing spatial objects (point, line, and area features) as an underlying graph of topological primitives—nodes, faces, and edges. representing the feature geometries.  Topology is fundamentally used to ensure data quality of the spatial relationships and to aid in data compilation. Topology is also used for analyzing spatial relationships in many situations, such as dissolving the boundaries between adjacent polygons with the same attribute values or traversing a network of the elements in a topology graph. Topology can also be used to model how the geometry from a number of feature classes can be integrated. Ways that features share geometry in a topology Features can share geometry within a topology. Here are some examples among adjacent features: Area features can share boundaries (polygon topology). Line features can share endpoints (edge-node topology). In addition, shared geometry can be managed between feature classes using a geodatabase topology. For example: Line features can share segments with other line features. Area features can be coincident with other area features. For example, parcels can nest within blocks. Line features can share endpoint vertices with other point features (node topology). Point features can be coincident with line features (point events). Elements of GDB Topology In geodatabases, topology is the arrangement that defines how point, line, and polygon features share coincident geometry. In a geodatabase, the following properties are defined for each topology: ❖ The name of the topology to be created. ❖ The cluster tolerance used in topological processing operations. The cluster tolerance is often a term used to refer to two tolerances: the x,y tolerance and the z-tolerance. The default value for the cluster tolerance is 10 times the coordinate resolution. ❖ List of feature classes. First, you need a list of the feature classes that will participate in a topology. All must be in the same coordinate system and organized into the same feature dataset. ❖ The relative accuracy rank of the coordinates in each feature class. If some feature classes are more accurate than others, you will want to assign a higher coordinate rank. This will be used in topological validation and integration. Coordinates of a lower accuracy will be moved to the locations of more accurate coordinates when they fall within the cluster tolerance of one another. Features with the highest accuracy should receive a value of 1, less accurate feature classes a value of 2, even less accurate feature classes a value of 3, and so on. ❖ A list of topology rules for how features share geometry. Topology rules Topology rules define the permissible spatial relationships between features. The rules you define for a topology control the relationships between features within a feature class, between features in different feature classes, or between subtypes of features. For example, the rule Must not overlap is used to manage the integrity of features in the same feature class. If two features overlap, the overlapping geometries are displayed in red (such as shown by the overlapping red area in the adjacent polygons and the linear segment of the two lines below).  Topology rules can also be defined between subtypes of feature classes. For example, suppose you have two subtypes of street line features—normal streets (those that connect to other streets at both nodes) and cul-de-sac streets (those that dead-end at one node). A topology rule can require street features to be connected to other street features at both ends, except in the case of streets belonging to the cul-de-sac subtype. Use your features' spatial relationships and behavior to define topology rules Spatial relationships express specifically how features share coincident geometry along with the rules for the behavior of their spatial representations. For example, some common spatial relationships and rules include the following: ▪ Parcels cannot overlap. Adjacent parcels have shared boundaries. ▪ Stream lines cannot overlap and must connect to one another at their endpoints. ▪ Adjacent countries have shared edges. Countries must completely cover and nest within states. ▪ Adjacent Census Blocks have shared edges. Census Blocks must not overlap, and Census Blocks must completely cover and nest within Block Groups. ▪ Road centerlines must connect at their endpoints. ▪ Road centerlines and Census Blocks share coincident geometry (edges and nodes). Topology errors and exceptions Violations of topology rules are initially stored as errors in the topology. Error features record where topological errors were discovered during validation. Certain errors may be acceptable, in which case the error features can be marked as exceptions. Errors and exceptions are stored as features in the topology layer and allow you to render and manage the cases in which features need not adhere to the topology rules.  You can create a report of the errors and exceptions for the feature classes in your topology. You can use the report of the number of error features as a measure of the data quality of a topological dataset. The Error Inspector in ArcMap lets you select different types of errors and zoom to individual errors. You can correct topology errors by editing the features that violate the topology's rules. After you validate the edits, the error is deleted from the topology. The editing tools allow you to select a topology error and choose from a number of fixes that have been predefined for that error type. You can also use the tool to get more information about the rule that has been violated or mark the error as an exception.  Geodatabase topologies are flexible enough to handle exceptions to the topology rules. You can also mark errors as exceptions. Exceptions are thereafter ignored, although you can return them to error status if you decide that they are actually errors and that the features should be modified to comply with the topology rules. Exceptions are a normal part of the data creation and update process. For example, a street database for a city might have a rule that centerlines must connect at both ends to other centerlines. This rule would normally ensure that street segments are correctly snapped to other street segments when they are edited. However, at the boundaries of the city, you might not have street data. Here, the external ends of streets might not snap to other centerlines. These cases could be marked as exceptions, and you would still be able to use the rule to find cases where streets were incorrectly digitized or edited. Dirty areas and validation A key goal of geodatabase topologies is to optimize the time spent on processing and validating the feature data that participates in a topology before it can be used. ✓ Feature classes that participate in a topology are always available for use regardless of the state of the topology. ✓ Topology validation is user driven. You decide when and how often you want to validate the topology (for example, after every edit operation or less frequently such as at the end of each edit session). ✓ All edits made to each feature class are tracked so that only the areas in which changes have been made need to be revalidated. ✓ Dirty areas are areas that have been edited, updated, or affected by the addition or deletion of features. Dirty areas allow the topology to limit the area that must be checked for topology errors during topology validation. Dirty areas track the places where new features have been added or existing features modified. This allows selected parts, rather than the whole extent of the topology, to be validated. Dirty areas are managed for you by ArcGIS Dirty areas are created by ArcGIS when a feature that participates in a topology is created or deleted, a feature's geometry is modified, a feature's subtype is changed, versions are reconciled, the topology properties are modified, or the geodatabase topology rules are changed. Version reconciliation acts like other edits and updates to a feature class—the changed areas are flagged as dirty. Schema changes, such as adding a new topology rule, imply that the whole topology must be revalidated (in other words, the whole dataset is flagged as dirty). Metadata Any data about the organization’s data resource [Brackett 2000, p. 149]. All physical data and knowledge from inside and outside an organization, including information about the physical data, technical and business processes, rules and constraints of the data, and structures of the data used by a corporation [Marco 2000, p. 5]. The detailed description of instance data. The format and characteristics of populated instance data: instances and values, dependent on the role of the metadata recipient [Tannenbaum 2002, p. 93].  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. Metadata: A part of Geographic Data Metadata is the third component of geographic data. Geospatial data tells you where it is and attribute data tells you what it is. Metadata describes both geospatial and attribute 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 Query Queries are essentially questions posed to a database. The selective display and retrieval of information based on these queries are essential components of any geographic information system (GIS). There are three basic methods for searching and querying attribute data: (1) selection, (2) query by attribute, and (3) query by geography. 1. Selection Selection represents the easiest way to search and query spatial data in a GIS. Selecting features highlight those attributes of interest, both on-screen and in the attribute table, for subsequent display or analysis. To accomplish this, one selects points, lines, and polygons simply by using the cursor to “point-and-click” the feature of interest or by using the cursor to drag a box around those features. Alternatively, one can select features by using a graphic object, such as a circle, line, or polygon, to highlight all of those features that fall within the object. Advanced options for selecting subsets of data from the larger dataset include creating a new selection, selecting from the currently selected features, adding to the current selection, and removing from the current selection. 2. Query by Attribute Map features and their associated data can be retrieved via the query of attribute information within the data tables. For example, search and query tools allow a user to show all the census tracts that have a population density of 500 or greater, to show all counties that are less than or equal to 100 square kilometers, or to show all convenience stores within 1 mile of an interstate highway. Specifically, SQL (Structured Query Language) is a commonly used computer language developed to query attribute data within a relational database management system. Created by IBM in the 1970s, SQL allows for the retrieval of a subset of attribute information based on specific, user-defined criteria via the implementation of particular language elements. More recently, the use of SQL has been extended for use in a GIS (Shekhar and Chawla 2003).Shekhar, S., and S. Chawla. 2003. Spatial Databases: A Tour. Upper Saddle River, NJ: Prentice Hall. One important note related to the use of SQL is that the exact expression used to query a dataset depends on the GIS file format being examined. For example, ANSI SQL is a particular version used to query ArcSDE geodatabases, while Jet SQL is used to access personal geodatabases. Similarly, shapefiles, coverages, and dBASE tables use a restricted version of SQL that doesn’t support all the features of ANSI SQL or Jet SQL. 3. Query by Geography Query by geography, also known as a “spatial query,” allows one to highlight particular features by examining their position relative to other features. For example, a GIS provides robust tools that allow for the determination of the number of schools within 10 miles of a home. Several spatial query options are available, as outlined here. Throughout this discussion, the “target layer” refers to the feature dataset whose attributes are selected, while the “source layer” refers to the feature dataset on which the spatial query is applied. For example, if we were to use a state boundary polygon feature dataset to select highways from a line feature dataset (e.g., select all the highways that run through the state of Arkansas), the state layer is the source, while the highway layer is the target. Generalization Tools in the Generalization toolset can be used to aggregate or eliminate features. Dissolve Aggregates features based on specified attributes. Eliminates polygons by merging them with neighboring polygons that have the largest area or the longest shared border. Eliminate Eliminate is often used to remove small sliver polygons that are the result of overlay operations, such as Intersect or Union. Creates a new output feature class containing the features from Eliminate the input polygons with some parts or holes of a specified size Polygon Part deleted.  Fig: Dissolve Fig: Eliminate Polygon Part Fig: Eliminate Buffering Buffering is the process of creating an output polygon layer containing a zone (or zones) of a specified width around an input point, line, or polygon feature. Buffers are particularly suited for determining the area of influence around features of interest. Geoprocessing is a suite of tools provided by many geographic information system (GIS) software packages that allow the user to automate many of the mundane tasks associated with manipulating GIS data. Geoprocessing usually involves the input of one or more feature datasets, followed by a spatially explicit analysis, and resulting in an output feature dataset. Buffers are common vector analysis tools used to address questions of proximity in a GIS and can be used on points, lines, or polygons. Geoprocessing Operations “Geoprocessing” is a loaded term in the field of GIS. The term can (and should) be widely applied to any attempt to manipulate GIS data. However, the term came into common usage due to its application to a somewhat arbitrary suite of single layer and multiple layer analytical techniques in the Geoprocessing Wizard of ESRI’s ArcView software package in the mid-1990s. Regardless, the suite of geoprocessing tools available in a GIS greatly expand and simplify many of the management and manipulation processes associated with vector feature datasets. The primary use of these tools is to automate the repetitive preprocessing needs of typical spatial analyses and to assemble exact graphical representations for subsequent analysis and/or inclusion in presentations and final mapping products. 1. The dissolve operation combines adjacent polygon features in a single feature dataset based on a single predetermined attribute. The dissolve tool automatically combines all adjacent features with the same attribute values. The result is an output layer with the same extent as the original but without all of the unnecessary, intervening line segments. The dissolved output layer is much easier to visually interpret when the map is classified according to the dissolved field. 2. The append operation creates an output polygon layer by combining the spatial extent of two or more layers. Unlike the dissolve tool, append does not remove the boundary lines between appended layers (in the case of lines and polygons). Therefore, it is often useful to perform a dissolve after the use of the append tool to remove these potentially unnecessary dividing lines. Append is frequently used to mosaic data layers, such as digital US Geological Survey (USGS) 7.5-minute topographic maps, to create a single map for analysis and/or display. 3. The select operation creates an output layer based on a user-defined query that selects particular features from the input layer. The output layer contains only those features that are selected during the query. For example, a city planner may choose to perform a select on all areas that are zoned “residential” so he or she can quickly assess which areas in town are suitable for a proposed housing development. 4. Finally, the merge operation combines features within a point, line, or polygon layer into a single feature with identical attribute information. Often, the original features will have different values for a given attribute. In this case, the first attribute encountered is carried over into the attribute table, and the remaining attributes are lost. This operation is particularly useful when polygons are found to be unintentionally overlapping. Merge will conveniently combine these features into a single entity. Single Layer Geoprocessing Functions Map projections refer to the methods and procedures that are used to transform the spherical three-dimensional earth into two-dimensional planar surfaces. Specifically, map projections are mathematical formulas that are used to translate latitude and longitude on the surface of the earth to x and y coordinates on a plane. Since there are an infinite number of ways this translation can be performed, there are an infinite number of map projections. The mathematics behind map projections are beyond the scope of this introductory overview (but see Robinson et al. 1995; Muehrcke and Muehrcke 1998),Muehrcke, P., and J. Muehrcke. 1998. Map Use. Madison, WI: JP Publications. and for simplicity, the following discussion focuses on describing types of map projections, the distortions inherent to map projections, and the selection of appropriate map projections. The Concept of Map “Projection” Projections and Transformations toolset This toolset contains tools to convert geographic data from one map projection to another. When you obtain GIS data, it often needs to be transformed or projected. Since the data you receive is not always preprocessed, you will often need to place coordinates to your raster image. The transformation tools in the Projections and Transformations toolset can be used to rectify these issues. Whether you treat the earth as a sphere or a spheroid, you must transform its three- dimensional surface to create a flat map sheet. This mathematical transformation is commonly referred to as a map projection.  To understand how transformations work, you must keep in mind that all places on the earth have a location, and spatial data corresponds to one of these locations. Imagery and raster data that are not preprocessed—meaning that they come straight from the sensor or scanner—will usually not have any of these coordinates or locations inherent. The transformation tools are responsible for warping the image to the proper location and changing the image to the proper orientation. Altering spatial properties using map projections can be described as shining a light through the earth onto a surface called the projection surface. Imagine that the earth's surface is clear, with the graticule drawn on it. Wrap a piece of paper around the earth.  A light at the center of the earth will cast the shadows of the graticule onto the piece of paper. You can now unwrap the paper and lay it flat. The shape of the graticule on the flat paper is very different from what it was on the earth because the map projection has distorted the graticule. A spheroid can't be flattened to a plane any more easily than a piece of orange peel can be flattened; it will rip. Representing the earth's surface in two dimensions causes distortion in the shape, area, distance, or direction of the data. A map projection uses mathematical formulas to relate spherical coordinates on the globe to flat, planar coordinates. Different projections cause different types of distortions. Some projections are designed to minimize the distortion of one or two of the data's characteristics. A projection could maintain the area of a feature but alter its shape. Tool Description Changes the coordinate system of a set of input feature classes or feature Batch Project datasets to a common coordinate system. To change the coordinate system of a single feature class or dataset use the Project tool. Convert Coordinate Converts coordinate notations contained on one or two fields from one Notation notation format to another. Creates a transformation method for converting data between two geographic Create Custom coordinate systems or datums. The output of this tool can be used as a Geographic transformation method for any tool with a parameter that requires a Transformation geographic transformation. Create Spatial Creates a spatial reference for use in ModelBuilder. Reference Overwrites the coordinate system information (map projection and datum) Define Projection stored with a dataset. This tool is intended for datasets that have an unknown or incorrect coordinate system defined. Project Projects spatial data from one coordinate system to another. Topology is a set of rules that model the relationships between neighboring points, lines, and polygons and determines how they share geometry. For example, consider two adjacent polygons. In the spaghetti model, the shared boundary of two neighboring polygons is defined as two separate, identical lines. The inclusion of topology into the data model allows for a single line to represent this shared boundary with an explicit reference to denote which side of the line belongs with which polygon. Topology is also concerned with preserving spatial properties when the forms are bent, stretched, or placed under similar geometric transformations, which allows for more efficient projection and reprojection of map files.Three basic topological precepts that are necessary to understand the topological data model are outlined here. First, connectivity describes the arc-node topology for the feature dataset. The second basic topological precept is area definition. Area definition states that an arc that connects to surround an area defines a polygon, also called polygon-arc topology. Contiguity, the third topological precept, is based on the concept that polygons that share a boundary are deemed adjacent.  Editing Features in Arc Map ✓ Editing occurs in an edit session. ✓ During an edit session, you can create or modify vector features or tabular attribute information. ✓ When you want to edit, you need to start an edit session, which you end when you're done. ✓ Editing applies to a single workspace in a single ArcMap data frame, where a workspace is a geodatabase or a folder of shapefiles. ✓ If you have more than one data frame in your map, you can only edit the layers in one data frame—even if all data is in the same workspace. ✓ Although you can edit data in different coordinate systems, it is generally best if all the data you plan to edit together has the same coordinate system as the data frame. ✓ There are two ways to start an edit session: by clicking the Editor menu on the Editor toolbar or by right-clicking a layer in the table of contents. ✓ If you use the Editor menu to start editing on a data frame that contains data from multiple workspaces, you are prompted to choose the workspace to edit. ✓ If you right-click a layer in the table of contents, you automatically start an edit session on the entire workspace containing that layer. ✓ Edits are temporary until you choose to save and apply them permanently to your data. ✓ You can also quit an edit session without saving your changes. ✓ Just saving a map document does not save the edits to the features—you need to specifically save the edits in your edit session. ✓ When you save edits, you write them to the data source, or a database. ✓ If you start editing on a data frame that contains data from multiple workspaces, the Start Editing dialog box appears so you can choose the one you want to edit. ✓ If there were only one folder or database in your data frame containing data you can edit, you would not see this dialog box because ArcMap would simply start your edit session on that folder or database. Thank you