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An enterprise electric distribution gis

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CHAPTER 4 GIS PATTERNS

An Enterprise GIS that participates in the transformation from the utility of today to the utility of the future is not a mapping system. It's an information system. As such it must be crafted with care and insight, especially if it's going to play such a pivotal role in the utility transformation. The purpose of this chapter is to outline patterns to give construction or updating of a legacy GIS some discipline and structure. In fact the GIS pattern for an Enterprise Electric Distribution GIS follows the same methodology as any other industry specific GIS. The pattern for any GIS (whether utility, public works, retail) consists of four strong patterns:

  • Data management - like managing the assets: sensors, poles, conduit, smart meters, trucks, people
  • Analysis and planning - what is the optimal placement of fault indicators to where are these places in my system that are susceptible to lighting strokes, or where do I plan for the next big wind farm.
  • Mobility - how do I collect data from the field and integrate that data with corporate dashboards
  • Situational awareness - visualizing the business spatially - like where are there customers who are have small houses and use lots of electricity.
DATA MANAGEMENT - THE DATA JUST HAS TO BE RIGHTBusiness Issues of Data Management

GIS is widely recognized for its strong role in managing traditional electric transmission and distribution and telecommunications networks. GIS provides the most comprehensive inventory of the electrical distribution network components and their spatial locations. With Smart Grid's sophisticated communication network superimposed on the electric network, data management with GIS becomes utterly critical. GIS not only manages the data itself, but it is critical in managing the data transactions. It's one thing to have a complete data base, it's quite another to have solid quality assurance processes and methods in place to make thatsure that data is up to date and accurate. One of the key aspects of Enterprise GIS is the work flows to keep the data right.

Energy companies are old. Some utilities are more than 120 years old. They have built information systems using manual processes. To manage the work load, companies created many independent departments such as engineering and plant accounting. The bureaucratic layers often inhibited a free flow of information. As these departments moved toward automation, they often created stand alone departmental systems with no mechanism to verify information consistency. While energy companies have improved and modernized their information systems, many problems persist.

Asset information for utilities is often incomplete, inaccurate, or inconsistent. Much of the asset management information is not easily verified. A significant portion of the world's electric distribution facilities are underground. Those facilities that are relatively difficult to verify and are widely dispersed throughout the service territory.

What is the best way to address these challenges? GIS is recognized for its strong role in managing traditional electric distribution networks.. GIS provides the most comprehensive inventory of the network components and their spatial locations. In short, GIS becomes utterly critical. GIS not only manages the data itself, but it manages data transactions. A solid quality assurance process can make sure that data is up-to-date and accurate. One of the key aspects of Enterprise GIS is the work flows designed to ensure data accuracy.

Utilities can rely on GIS-based data management to resolve problems that stem from poor data management. A GIS, by definition, is not a drafting system or a computer-aided design (CAD) system. Rather it is a database management system that responds to queries by providing results in the form of a map. When an operator asks a GIS, "Show me all the direct buried cables that are known to have failure history that have not been repaired in two years, and that occurred on roads that are scheduled to be repaved," the GIS responds with a map that highlights the appropriate cable sections.

GIS is not about creating a cleaner, clearer map from an old hand-drawn map. It is about discovering something new. That discovery should result in new, decisive action. In this simple example, the electric company can schedule replacement of old direct buried cables just prior to the city repaving the streets. Too often the opposite happens: The city repaves a street, and within months the electric company has to dig up the street to do a repair. The city then has to patch a newly repaved street. This common occurrence is both wasteful and inconvenient to the public. These events erode the credibility of the city and the utility. Furthermore, such mistakes make it more difficult for a utility to get facility siting or rate-increase approval. Public display of poor planning and misinformation can seriously erode a utility's ability to do its job.

How Data is Management Managed in the GIS

Data can be loaded into existing feature classes and tables in a geodatabase via ArcMap using the Object Loader, or via ArcCatalog using the Simple Data Loader. Both the Object Loader and Simple Data Loader allow you to load data into empty feature classes and tables, or add to feature classes and tables that already contain data. You can load coverage, shapefile, CAD, or geodatabase feature class data into an existing feature class, providing it falls within the spatial reference of the feature class you're loading into. You can load INFO, dBASE, or geodatabase table data into an existing table.

While the Object Loader and Simple Data Loader are similar loading wizards, the Object Loader provides the following functionality that the Simple Data Loader does not have:

Because the Object Loader loads data during an edit session in ArcMap, once you've finished loading, you can undo the changes if needed.

If the feature coordinates you're loading are not precisely located, you can choose to honor the current snapping environment, snapping coordinates as they load.

If you're loading into a feature class that has validation rules, such as attribute domain or geometric network connectivity rules, you can validate the features added and create a selection of the loaded features that are in violation of these rules.

With the Object Loader, you can load into feature classes in a geometric network, feature classes in a relationship with messaging, or feature classes that have feature-linked annotation. You cannot load into these types of feature classes with the Simple Data Loader.

If you don't require any of the above capabilities, you can load with the Simple Data Loader. The Simple Data Loader is faster because it doesn't validate or process data as it loads.

Managing the assets of a utility company is a complex task. The many components of an integrated electrical grid include generation stations, poles, transformers, transmission lines, sensors, and smart meters, to name a few. Each of these objects can be managed in a GIS by representing their geographic location as either a point, line or polygon, and assigning attribute information to each one, and relationship connections between them.

The initial design of a utility GIS involves the fundamental task of deciding what data is required to manage the system. The Electric Distribution Data Model (described in detail in chapter 7) defines a data and workflow structure that identifies the major components of a GIS specific to electrical distribution, to include structures, circuits and devices. It is a geographically oriented view of electric distribution systems that helps utility managers and administrators visualize, analyze, and understand real-world engineering and business problems to model and manage the flow of electricity more effectively.

Once the data elements of the electrical distribution system are identified, the data itself can be collected from a variety of sources. Old utility inventory databases, CAD drawings, and maps can be updated and converted to contemporary formats to be i mported in the GIS, as well as current field data gathered from GPS receivers. Embedding these data into a geodatabase enables the building of a integrated relational database where each element or object of the utility system is defined as a feature at a specific geographic x,y location associated to it's attributes. For example, a pole is represented as a single point (a feature) that is a member of a set of poles (a feature class), each having a set of attributes that describe it...its install date, height, etc.. Because the GIS keeps track of the location of each pole, this topology enables each pole to not only know where it is located, but where every other pole in its feature class is located in relation to itself. Other features, such as transmission lines, are also included in the same geodatabase, and because it is relational, connections can be made between poles and lines so that the system keeps track of which lines are supported by which poles. This relationship feature helps utility managers understand the behavior of all of the components in the system, enhancing their ability to manage the day to day flow of power, as well as address the impacts of outages within the system.

When utility data is embedded in a geodatabase, managers can easily retrieve, update and delete features within the distribution system as configurations change. Retrieving data for audits, report s, and managing work flows and maintenance personnel is easily managed within the GIS as long as the data is current and well maintained. Updating the data on an ongoing basis requires protocols that adhere to strict data management procedures. The GIS software environment supports such protocols by enabling the editing of features in the geodatabase, as well as the management of multiple versions, database compression, reconciliation, conflict resolution and replication that may arise from several users simultaneously interacting with the data within an organization, either from within the same office, different departments, or out in the field.

Editing:Web editing

Web applications are created for ArcSDE geodatabases using ArcGIS Server Manager. Once data is served over the web via a map, and it is symbolized appropriately, it can then be made available for web editing. Editing options are set for access to certain layers and versions; types of edits such as adding features, editing attributes, or editing features; and selection, snapping, and conflict resolution rules.

Editing in ArcMap

GIS software enables ArcMap is the application for creating and editing geographic data. ArcMap contains tools that help you construct features quickly and easily while maintaining the integrity of your data. For organizations that need multiple users to simultaneously edit a shared geodatabase, ArcMap and ArcSDE provide the tools necessary to manage versions and resolve potential conflicts.

ArcGIS allows you to create and edit several kinds of data. Editable feature geometry includes points, lines, polygons, text (annotations and dimensions), multipatches, multipoints, shared edges and nodes from a topology or geometric network, and tables. Edits are usually performed within an edit session where all additions and changes are temporary until the edit session is closed and the changes are saved.

Editing tasks include creating new data, modifying data, and working with topology. When cCreating or modifying the shape of features, you useis done in an "edit sketch," which is a temporary representation of a feature that allows you to edit it's geometry to be modified. A sketch is composed of all the vertices and segments of the feature. Vertices are the points at which the sketch changes direction such as corners; segments are the lines that connect the vertices.

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When you want to c Creatinge new features or drawing a sketch, areyou'll most commonly performed ininvoke an edit session and usinge the Sketch sketch and edittool and the tools on the Editor toolbar tool palette. With those tools, for example, you canto create lines, arcs, and tangent curves; vertices at intersections or midpoints; vertices based on distances and directions from other features; or new segments by tracing along existing ones.

In addition to constructing new features, sketches can be used to modify existing features. For example, to reshapinge a polygon involves, you can constructing a sketch of the new edge, or you can usinge a sketch to divide one polygon into two. You can iInserting or deletinge vertices in a sketch to modify modifies an existing feature's shape. You can also tTrimming, extending, and splitting lines and polygons are also done by drawing a sketch.

Attributes can also be added or modified during an edit session by entering new values manually or by copying and pasting existing values. Attribute changes can be made either in the Attributes dialog box or the table window. Annotation can also be created and edited in ArcMap using specialized annotation editing tools.

Editing processes can be streamlined and made more accurate by using some of the functions in the editing environment. Snapping enables more accurate positioning of new vertices and segments, and ensures that existing features are placed in the correct position when moved. Jumping, or snapping to, edges and vertices ensures that when creating polygons, they do not overlap or have gaps between them, and enable more exact placement of points along existing lines. Using the magnifier, overview, and viewer windows also enables more accurate feature editing.

Within the editing environment, the spatial adjustment tools provide interactive methods to align and integrate data. Spatial adjustment supports a variety of adjustment methods and will adjust all editable data sources. It's often used whenon you've imported data from another source, such as a CAD drawing or a 3D multipatch model. Some common editing of the tasks you can perform include converting data from one coordinate system to another, correcting geometric distortions, aligning features along the edge of one layer to features of an adjoining layer, and copying attributes between layers. Since spatial adjustment operates within an edit session, you can use existing editing functionality, such as snapping, to enhance your adjustments.

You can also C creatinge a map topology so that each feature knows it relative geographic location and connectivity to all other geographic features that enables you to the editing of the shared parts of features within the editing environment. A map topology is a temporary set of topological relationships between coincident parts of simple features on a map. The primary types of geometry that are acted on when editing a map topology are edges, which are line segments that define lines or polygons, and nodes-points at the end of an edge. When you move a node is moved in a topology, all the edges that connect to it are stretched to stay connected to the node. When you move an edge is moved, edge segments stretch to maintain the connection of shared endpoint nodes to their previous location. You can also move a Nnodes and a connected edges can also be moved without stretching the other connected edges by temporarily splitting the topological relationship between the node and the other shared edges.

Geodatabases made available over the Web via a map application can also be edited by options set for access to certain layers and versions. Web edits include adding features, editing attributes, or editing features; and selection, snapping, and conflict resolution rules.

Versioning

Versioning allows multiple users to edit the same data in a geodatabase without applying locks or duplicating data. A version represents a snapshot in time of the entire geodatabase and contains all of its datasets, as well as any of it's connectivity and flow attributes. A version isolates users' work across multiple edit sessions, allowing each user to edit without locking features in the production version or immediately impacting other users.

When you connecting to a multiuser geodatabase in ArcCatalog or ArcMap, the useryou specifiesy the version connected to which you will connect. By default, the useryou initially connects to the rootDEFAULT version of the geodatabase, which. The DEFAULT version is the root version and, therefore, becomes the ancestor of all other versions. This primary version is. u Unlike the other versions that are created by multiple users, the DEFAULT version in that it always exists and cannot be deleted. In most workflow strategies, it is the published version of the database, representing the current state of the system being modeled. MYou maintaining and updatinge this ancestore DEFAULT version over time is done by posting changes to it from other versions. UsersYou can also edit itthe DEFAULT version directly, just like any other version.

A You create a new version is created for use by creating children or branches from any existing version. You Tcreate the first version is created by making a child version of the ancestorDEFAULT version. When the new version is created, it is identical to the ancestorDEFAULT version. Over time, the versions will diverge as changes are made to the ancestorDEFAULT version and to the new version. You can create aAny number of versions can be created and can have users edit them simultaneously. Multiple users can also edit the same version at the same time.

In a GISArcCatalog or ArcMap, you can perform all of the same display, query, and editing functions can be done on a version as you as can be done on a nonversioned geodatabase. Once the editing of the version isyou're finished, editing a version, a useryou can integrate the changes into any version that is an ancestor, such as the parent or DEFAULT version. To integrate the changes, you must firstthey must first be reconciled them in the version you'rebeing editing with the version into which they will be mergedyou want to merge them, and then posted them tto the ancestor version.

Compression

As edits are made to a versioned geodatabase, the number of states (a version's lineage) and rows in the delta tables (the adds and deletes tables) grows significantly. This can slow database performance. Compression is a process that removes unreferenced rows from geodatabase system tables and user delta tables. Compression helps maintain versioned geodatabase performance.

As edits are made to a versioned ArcSDE geodatabase, the number of states (a version's lineage) and rows in the delta tables (the adds and deletes tables) grows significantly. This can slow database performance.

A version's lineage grows whenever edits are saved. Each save made in an edit session creates a state in the lineage that is not trimmed until the database is compressed. To avoid performance degradation, periodic compression of the versioned geodatabase is necessary. The cCompressing the geodatabase command removes the states that are no longer referenced by a version and can move rows in the delta tables to the base table.

Reconciliation

Reconcile merges all modifications between the current edit version and a target version. Any differences between the features in the target version and the features in the edit version are applied to the edit version. Differences can consist of newly inserted, deleted, or updated features. The reconcile process detects these differences and discovers any conflicts. Reconciling happens before posting a version to a target version. A target version is any version in the direct ancestry of the version such as the parent version or the default versionthat contains all changes made to the data.

Conflict management

Conflicts are managed and resolved within a database when two versions of the same data are edited at the same time. Conflicts can occur when multiple users simultaneously edit the same feature or topologically related features, or reconcile two versions of a dataset. Resolving a conflict requires that the user make a decision about the feature's correct representation.

Conflicts can be resolved at several different levels: field level (attribute), row (individual feature), class (entire feature class), or root (all conflicts in all feature classes and features for a particular reconcile operation).

Replication

Geodatabase replication is a data distribution method provided through ArcGISa GIS. With geodatabase replication, data is distributed across two or more geodatabases by replicating all or part of your a dataset. When a dataset is replicated, two replicas are created: one that resides in the original geodatabase, and a related replica that is distributed to a different geodatabase. Any changes made to these replicas in their respective geodatabases can be synchronized so that the data in one replica matches that in the related replica.

Geodatabase replication is built on top of the versioning environment and supports the full geodatabase data model including topologies, networks, terrains, relationships, etc. In this asynchronous model, the replication is loosely coupled, meaning that each replicated geodatabase can work independently and still synchronize changes with one another. Since it is implemented at the geodatabase level, the DBMSs involved can be different. For example, one replica geodatabase could be built on top of SQL Server and the other on top of Oracle.

Geodatabase replication can be used in connected and disconnected environments, such as mobile GIS users. It can also work with local geodatabase connections, as well as geodata server objects that allow you togodatabases accessed via access a geodatabase on the Internet.

Geodatabase replication can be used to create replica trees, similar to version trees, allowing organizations to distribute their data across several geodatabases in a hierarchical structure. For example, some organizations require the ability to replicate a single organization-wide geodatabase across different offices. Each office has a replica with only the data applicable to its area and can transfer changes of this data to the main office. This allows the main office to perform analysis on data that is up-to-date across the entire extent. Connections are fast within an office, but are much slower between offices. The regional offices can also replicate their geodatabase to local offices in the same way that the main office replicates its geodatabase to the regions.

A replica geodatabase can also be used as a central hub to host readers and editors. To keep connection speeds fast, editors can create a replica to check out data from the central hub and perform edits, then check the changes back in by synchronizing with the geodatabase.

The central hub can also be used to propagate changes between multiple child replicas. To move changes from one replica to another, the changes in one replica are first synchronized with the parent (or hub) replica. A second child replica can then synchronize with the parent to get these changes.

Federated data management

Federated databases provide a means of combining data from several source databases into a single database. Federated systems combine heterogeneous servers and services for collaboration across organizations.

Database and Web technology standards provide new opportunities to better manage and support user access to a rapidly growing volume of geospatial data resources. Web services and rich XML communication protocols enable efficient data migration between distributed databases and centralized storage locations. Web search engines and standard Web mapping services provide a means to discover and consume integrated geospatial information products published from a common portal environment with data provided from a variety of distributed service locations. Federated architectures promote better data management by integrating community and national GIS operations.

Geodatabase replication services and managed extract, transform, and load (ETL) processes support loosely coupled distributed geodatabase environments.

Federated systems are composed of parties that share networked applications and data resources. Many local government, state, and federal agencies share GIS data to support community operations. Data resources owned by each party are brought together to provide community-level information products. Data maintenance responsibilities are distributed between different groups, with databases configured to share GIS resources between the different sites on a scheduled basis. Web portals provide applications supported by a variety of Web services and act as a broker to connect users with published community-wide data services.

ANALYSIS - What is the data telling me?

In the case of the Smart Grid, it is composed of two networks-electric and communications-utilities must understand physical and spatial relationships among all network components. These relationships will form the basis for some of the advanced decision making the Smart Grid makes. Smart Grid must have a solid understanding of the connectivity of both networks. GIS provides the tools and workflows for network modeling and advanced tracing.

GIS is used to determine optimal locations for Smart Grid components. During the rollout of Smart Grid, utilities will need significant analysis to determine the right location for sensors, communication marshalling cabinets, and a host of other devices such as fiber optics in conduit and on poles. GIS provides the proper means to perform these design services, since the optimal locations depend so heavily on the existing infrastructure. GIS can provide a spatial context to the analytics and metrics of Smart Grid. With GIS, utilities can track the metrics over time and provide a convenient means of visualizing trends. Since Smart Grid is supposed to be smart, it must be able to provide advanced grid performance analytics, track trends in equipment performance and customer behavior, and record key performance metrics.

Even with good information, companies don't always have a methodical process of analyzing the data. With inaccurate, out-dated and inconsistent data, analysis becomes impossible. Many utility compensate by building work-around work flows. For example, a utility should know how to determine the proper location to install a transformer. Without accurate data, the company will do analysis in the office and validate by taking field measurements. Utilities create job classifications to ensure that the information on the record accurately reflects the information in the field. An underground inspector, for example, checks a proposed cable route with field-based analysis based on a physical check of available empty duct positions. The inspector has to climb into every manhole to see whether the empty conduit specified by the designer is still empty. If it is not empty, the inspector has to find an alternate route. This course of action might take several weeks to complete. The work flow is expensive and could be replaced with an analytical process that uses a routing algorithm and accurate data-a process GIS can perform in seconds.

Since such complex problems have spatial components, companies may not have the proper analytical tools they need for guidance. For example, an electric company has to make a decision about how to prepare for an impending storm. It knows the distribution system has strengths and weaknesses. It knows that some tree trimming was performed recently in some areas and not in others. It knows that some of the equipment is old and some is new. It knows where there are outstanding maintenance work orders. It also knows the history of failures for some of that equipment. It also knows a lot more. The problem is that the utility manager must decide how many crews to deploy to which areas in order to minimize customer outage in the most cost effective way. The utility manager must make a quick decision based on the information at hand. With spatial analysis, the manager could rely on more than disparate information and gut instincts. If all the information could be assessed together, the manager would discover the most vulnerable areas given all the known factors.

The power of GIS is that it helps electric distribution companies understand the relationship of its assets to each other and to the surrounding environment. The ability of the GIS to perform complex analysis mitigates some of the risk of retiring employees. These older employees know a lot about the system's condition. When a thunderstorm is on the horizon, seasoned employees will instinctively know which parts of the system are more vulnerable than other parts. They carry utility data in their heads having seen the condition of the system with their own eyes. When these workers retire, all of that mental analysis goes with them. While GIS can't replace human intuition, it can provide the tools to qualify what people know to be true. A GIS can consume Web services such as predictive weather. It can determine from inspection information which facilities have been maintained and which have not. A GIS can tell you whether tree trimming is current. It can capture information about infrastructure age and material. In the end, the GIS can provide a vulnerability assessment that shows exactly where the distribution system is most vulnerable, and where the storm will hit. This spatial analysis can then be used to stage crews and minimize customer impact.

As things get more complicated, GIS becomes even more important. For example, the smart grid is composed of two networks-electric and communications. Utilities must understand physical and spatial relationships among all network components. These relationships will form the basis for some of smart grid's advanced decision making. Smart grid must have a solid understanding of the connectivity of both networks. GIS provides the tools and workflows for network modeling and advanced tracing. GIS is used to determine optimal locations for smart grid components. During the rollout of smart grid, utilities will need significant analysis to determine the right location for sensors, communication marshalling cabinets, and a host of other devices such as fiber optics in conduit and on poles. Since optimal device locations depend so heavily on the existing infrastructure, utilities will rely on GIS to support design services. GIS can provide a spatial context to the analytics and metrics of smart grid. With GIS, utilities can track the metrics over time and provide a convenient means of visualizing trends. Since smart grid is supposed to be smart, it will need GIS to provide advanced performance analytics, track trends in equipment and customer behavior, and record key metrics.

A lack of spatial analytical tools is solved with the GIS, as long as the data is accurate. Good data management, coupled with solid analytics, gives utilities the ability to make decisions about all kinds of business work flows, from engineering to financial to legal to customer service.

A GIS help analyze, for example, the relationship between frequent power outages and customer satisfaction based on a recent survey. It is a matter of a simple GIS query: "Show me on a map where there have been power failures and show me also where the customer satisfaction surveys show lower than average customer satisfaction." If the result of the query shows a spatial correlation, you can deduce that lower customer satisfaction numbers correlate with higher outages. But, if you further analyze places where meters are not read or where bills are high, you might gain further insight into customer dissatisfaction. More factors, such as utility construction could be added to the analysis. Without spatial analysis, a utility could come to the wrong conclusion as to why customers are not happy with a utility. By performing a spatial analysis, things become clear. The result could show that customers in a particular area rated the utility lower because their calls received a higher number of busy signals. Utilities may be making decisions based on false assumptions and thereby ignore the real cause of customer frustration. GIS performs these necessary and complex analyses, and presents results in the form a map which enables better decision making for utilities.

The Technical Aspects of Spatial AnalysisAnalysis[i]

GIS analysis is a process for looking at geographic patterns in data and at relationships between features. Once geographic features are mapped you can begin to understand why things are where they are. Applying analysis methods to study quantities and classes of features in certain locations reveals patterns in the data to understand the relationships between places.

Spatial Analysis

Performing analysis involves the process of framing a question, understanding the data, choosing an analysis method, processing the data, and looking at the results. Simple analysis methods can include making a map by representing features using symbols and classifications created using the following methods:

  • Mapping counts and amounts by showing the actual numbers of features on the map to see the value of each feature, as well as it's magnitude compared to other features. Examples include mapping the businesses in an area by the number of employees, or the annual precipitation in a region.
  • Using ratios to show the relationship between two quantities by evening out the differences between large and small areas, or areas with many features and those with few, so the map more accurately shows the distribution of features. The most common ratios are averages, proportions and densities. Examples include people per household by census tract (population/household), or the population per square mile (population/square miles) to show density by census tract.
  • Assigning rankings to put features in order from high to low, showing relative values rather than measured values. Rankings are indicated by either text values (high, medium, low) or numeric (1 through 10). Examples include assigning a ranking order to electricity distribution lines by capacity, or gas pipelines by condition.
  • Using classes of features to group them together by similar values. Classes can be created manually, or by using classification schemes such as Natural Breaks (Jenk's), Quantile, Equal Interval, and Standard Deviation. Certain classification schemes are better than others for particular types of data and analysis. Examples include using Natural Breaks for mapping data values that are not evenly distributed, since it places clustered values in the same class; or Standard Deviation to see which features are above or below an average value.

More complex geographic patterns may remain obscure when simply mapping features using symbols or classifications. Spatial analysis and modeling are ways to apply more sophisticated geometric and statistical techniques to data to reveal patterns and relationships that may not be initially evident. Querying the data to find out what's inside an area or what features may be within a certain distance of other features involves asking questions using a set of analysis tools, such as:

  • Select: Choosing from a number or group of features or records to create a separate set or subset. Examples include querying a database to identify schools that are within a certain straight-line distance of a particular pipeline; or census tracts where the median income is below a certain level where residents might benefit from public utility assistance programs.
  • Buffer: Creating a zone around a map feature measured in units of straight-line distance or time to determine what features are inside or outside of the zone. Examples include identifying all the open spaces that can serve as assembly areas outside of an evacuation zone defined by a buffer around a high consequence area where a natural gas or hazardous liquid release could have significant consequences; or establishing a safety buffer around transmission towers and along transmission corridors where search and rescue aircraft cannot land in case of emergency.
  • Overlay: Combining two or more maps or layers registered to a common coordinate system for the purpose of showing the relationships between features that occupy the same geographic space. Processing overlaid data results in either a new intersected layer where only the areas of common geographic space are retained; or a new unioned layer where the combined extent of all of the input layers becomes the new boundary of the layer. Examples include intersecting a floodplain layer with a parcel map to create a new layer of all parcels within the floodplain; or unioning a layer of the electricity grid with a layer of the natural gas pipeline network to get the full extent of serviced land in a community.

In addition to the mapped results of spatial analysis, the data can be processed to provide statistics of the selected features. Tabular data of addresses are useful when residents of a selected area need to be notified about emergencies or changes to services. Statistical summaries can also be generated using GIS tools to tabulate counts, frequencies, sums, averages, medians and standard deviations of mapped data, such as the total length of pipeline in a community, or the average kWh usage of electricity at a particular time of day in a subdivision.

Pattern Recognition

Spatial statistics can describe the characteristics of a set of features, such as the center of the features, the extent to which features are clustered or dispersed around the center (its compactness), and any directional trend that may exist (its orientation).

Centrality

Centrality is useful for finding a good location for something that has to be centrally located. For example, finding the center of recorded wildlife observations in a national park over the winter months may assist park planners in siting a winter ranger station or observation vista point. The mean center, median center, and central feature are all calculations used to determine centrality either on the unweighted basis of location alone, or as influenced by a weighted attribute value. An unweighted center is often used for incidents or events that occur at a place and time, such as pipeline ruptures. A weighted center is often calculated for stationary features, such as factories. A utility planner scouting locations for substations may calculate the weighted center using industrial density along with available fuel sources or access to transportation lines to ensure that the substation is as reasonably close the industrial parks as possible.

Measuring the compactness of a distribution provides a single value representing the dispersion of features around the center. To calculate the compactness of a distribution, the GIS measures the average distance the features or events vary from the mean center. The measure is called the standard distance deviation, or simply standard distance. The standard distance is a value, so the compactness can be represented on a map by drawing a circle with the radius equal to that value. For example, the dispersion of events of vandalism to equipment along an electrical network over a period of time can be represented by a circle around the mean.

Standard distance values can be used to compare two or more distributions. A crime analyst, for example, could compare the standard distance of vandalism to electrical equipment and other incidents of vandalism to public infrastructure. If the distribution of vandalism events in a particular area is compact, stationing a single car near the center of the area might suffice. If the distribution is dispersed, having several patrol cars stationed throughout the area might be more effective to increase response time.

Standard distance can also be weighted by attribute values associated with features. The standard distance can be calculated to determine the distribution of power generating plants based solely on location; but, those same plants can be further analyzed by their generating capacity to determine if larger or smaller plants are located closer to the center of a service area.

Orientation and direction

Measuring orientation and direction reveals the spatial trends in the distribution of features. Measuring the orientation of points and areas is different than for line features. For points and areas, the calculation used is similar to the standard distance circle that calculates the variance separately for the x and y coordinates. The result is displayed as an ellipse (known as a standard deviational ellipse) showing the orientation of the distribution. By calculating the orientation of a group of pipeline ruptures, for example, a utility analyst may find that the pattern of ruptures follows a directional trend similar to that of the fault lines in the area.

To determine the orientation of line features, a line is calculated showing the average angle of all of the line features. For example, the direction of transmission lines can be determined to see how they correlate to the orientation of lowlands and valleys in a region.

Pattern

Spatial statistics can also measure whether and to what extent the distribution of features creates a pattern. Geographic patterns range from completely clustered at one extreme to completely dispersed at the other. A pattern that falls at a point between these extremes is said to be random.

The measure of distribution can be used to compare patterns for different sets of features, or to compare how the same set of features changes over time. When using statistics to measure patterns, the actual distribution of features (often referred to as the observed distribution) is compared to a hypothetical random distribution of the same number of features over the same area. The GIS calculates the statistic for the observed distribution as well as what the statistic would be for a random distribution. The extent to which the observed distribution deviates from the random distribution is the extent to which the pattern is more clustered or more dispersed from the random distribution. The statistical value is a measure of the degree of clustering or dispersion.

Using statistics to measure patterns is more accurate than identifying patterns by looking at a map. On a thematic map, the classification method used, the number of classes, and the class ranges all affect whether a pattern appears or not. Since statistical tools use the underlying value of each feature, identifying a pattern does not depend on how the values are classified or displayed. If point features are close together on a small scale map, or multiple events occur at a single location, such as several reports of power outages from a multi unit apartment building, they will all appear as a single feature on a map. Unless the points close together are viewed at a zoomed-in scale, and the multi-event points are mapped using graduated symbols to show magnitude, the true distribution and weight of these events may be lost in a map display.

Measuring patterns is dependent on the extent and scale of the study area. Patterns may be dispersed when analyzed in a small study area, but clustered when studied in a wider, more extensive region. For example, certain wildlife habitats may appear clustered when studied statewide, but no pattern may be discernable when viewed at the county level. Scale also impacts how statistics are calculated across a study area. Global statistics focus on whether or not features form a pattern across the study area, and on what type of pattern it is. Local statistics focus on individual features and their relationship to nearby features.

The actual pattern of feature locations can be measured by a series of methods:
  • Overlaying areas of equal size: In this method, termed quadrant analysis, the GIS overlays areas of equal size on the study area and counts the number of features in each quadrant. It then compares each count to what it would be for a hypothetical random distribution. If fewer areas than expected contain most of the features, and more than expected contain few or no features, then the features form a clustered pattern.
  • Calculating the average distance between features: This method, termed the nearest neighbor index, measures the distance between each feature and its nearest neighbor, and then calculates the average. Distributions that have a smaller average distance between features than a random distribution would have are considered clustered.
  • Counting the number of features within a defined distance: In this method, known as the K-function, you specify a distance interval, and the GIS calculates the average number of neighboring features within that distance of each feature. Increasing distances at the specified interval show at what distance the concentration of features is greatest. If the average number of features found at a distance is greater than the average concentration of features throughout the study area, then distribution is considered clustered at that distance.
Geographic Relationships

Beyond analyzing how geographic features are distributed, GIS analysis can be used to analyze the relationships between features. When there is a discernable geographic pattern to features, it usually means that those features have a relationship with other features in the region that may be causing the pattern. Identifying and measuring relationships leads to a better understanding of what's going on in a place, predictions of where something is likely to occur, and why things occur where they do. Using spatial statistics allows you to verify a relationship, and to measure how strong it is. Methods that measure the geographic relationships between features include:

  • Spatial Autocorrelation: the notion that things near each other are more alike that things far apart. In addition to discerning patterns formed by location, a GIS can also measure patterns of attribute values associated with features, such as power usage per census tract. These methods reveal whether similar values tend to occur near each other, or whether high and low values are interspersed. This analysis indicates whether the distribution of values is dependent on the spatial distribution of features - that is, whether particular values are likely to occur in one location, or are equally likely to occur at any location. When nearby features are more similar than distant features, there is said to be a positive spatial correlation and that the features are clustered. If neighboring features tend to be unlike each other, there is a negative spatial correlation, and the features are dispersed.
  • Geary's c and Moran's I: In addition to determining if features are spatially autocorrelated, a GIS can further determine just how similar they are. Geary's c and Moran's I use the magnitude of feature values to indentify and measure the strength of spatial patterns. Both methods compare values for neighboring features. Then they compare the difference in values between each pair of neighbors to the difference in values between all features in the study area. If the average difference between neighboring features is less than between all features, the features are clustered.
  • General G-statistic: Tells you whether either hot spots (clusters or high values) or cold spots (clusters of low values) exist in an area. It doesn't report where those locations are, but may provide confirmation that clusters exist that are worth looking for.
  • Pearson's Correlation Coefficient: measures the ratio of the joint variation of two variables (or attributes) to the total variation of the entire dataset. The numerator is the covariance of the two variables - the extent to which an increase in one results in a proportional increase (or decrease, for negative correlation) in the other. The value of the correlation coefficient ranges from 1, indicating a perfect direct relationship, to -1, a perfect inverse relationship.
  • Spearman's Rank Correlation Coefficient: measures the extent to which two lists of ranked values correspond. The coefficient is based on the difference in rank between each feature of the two variables.
Regression Analysis
  • Ordinary Least Squares (OLS): shows the relationship between two variables - the independent variable, x, which is used to predict, and the dependent variable, y, which is the one to be predicted. This bivariate regression analysis can be used to define the relationship between y and x, using the observed values, and then to predict the value of y for any value of x. OLS models do not account for regional variation in a study area, a critical factor in spatial analysis.
  • Geographically Weighted Regression (GWR): one of several spatial regression techniques increasingly used in geography and other disciplines to account for regional variation. GWR provides a local model of the variable or process under study by fitting a regression equation to every feature in the dataset. GWR constructs these separate equations by incorporating the dependent and explanatory variables of features falling within the bandwidth of each target feature. By running a regression for each location, regional variation in a study area is accounted for in the analysis.
Network Analysis

Network Analysis uses a network layer of features, such as streets or pipelines, and identifies all the lines within a given distance, time, or cost of a source location. Source locations in a network are often termed "centers," as they usually represent centers to or from which people, goods, or services travel along lines. Each line segment of a network is assigned or tagged with a measure of cost or impedance, be it distance to travel on a street based on labor, fuel and maintenance, or construction cost per foot of transmission lines along a remote corridor.

When using distance as the impedance, the GIS assigns segments to the nearest center within a specified maximum distance. When using costs as the impedance, each line of the network is tagged with a per-segment cost based on a per-unit cost constant calculated from external data, such as the cost associated with pipeline capacity or speed limits along roads.

The GIS can also find surrounding features along, or within, the areas covered by those lines. Once the GIS has identified all the segments within a distance or cost of a center, it can find out what's within the area covered by those segments. By creating a boundary to enclose the selected segments and then overlaying it on another layer containing surrounding features, the GIS is able to capture and count other features within the boundary, such as the total number of households within a 15 minute distance of a substation.

Network analysis also includes modeling the direction of flow through a network where the direction of movement is defined. For example, the flow of electricity in an electrical network is from the power generation station to the customers. Flow direction in a network is calculated from a set of sources and sinks. In the above example, electricity flow is driven by sources and sinks. Flow is away from sources, such as the power generation station, and toward sinks, such as a transformerssubstation. You can trace the flow in a geometric network upstream or downstream by placing flags and barriers at the starting and end points of a network segment, and identify the impacts that events may have on the direction of flow or upon elements on the network. For example, schools and shelters that are impacted by an electrical outage can be identified downstream from a disruption in service along the geometric network by isolating the affected circuits.

Change Detection

One of the greatest strengths of GIS is it's ability to dynamically map conditions as they exist in space at a particular time. Adding a dynamic temporal dimension also allows you to map how conditions change in places over time. Mapping where and how features move in space and time depicts how things behave, and helps to anticipate future conditions and events. The results of policies and actions can also be evaluated by studying how features and events change over time and space.

Time series data can be mapped using three methods:
  • Trends: change between two or more dates or times. Trends indicate whether something is increasing or decreasing, or the direction of a feature's movement.
  • Before and after: conditions preceding and following an event to observe the impact of an event.
  • Cycle: change over a recurring period of time, such as a day, month, or year. Cycles reveal patterns of behavior that may reoccur over regular time intervals.

Features that move can be mapped as they change location from one time to another on the landscape. They might be individual features that can be mapped along paths, such as hurricanes, vehicles or animals; linear features, such as stream channels that change position, or pipelines that are relocated; or an area feature, such as a boundary of a fire that can be delineated as it moves through a region.

Events, such as crimes or power outages, represent geographic phenomena that occur at different locations. While each individual event occurs at a specific location at a specific instant, the set of events can be tracked and mapped to show the movement of phenomena over a period of time. For example, by mapping the distribution of calls to the main utility dispatch, the power company can identify which circuits are impacted by an event, such as a lightening strike or fallen tree, that has caused a disruption in service.

Features that change in character or magnitude can be mapped by an attribute associated with them. Land use changes in a community can be mapped as they transform over time, as well as the demand for power in a city from day to night. Calculating a change in an attribute value over a period of time, such as the population of counties over a 10 year period, shows not only which counties have had changes in their population, but how much and how fast these change occurred.

Maps of features and events can be strung together to compile time series maps that tell the story of change. A range of dates is displayed best by using fewer maps farther apart in time to make the change in values easier to see, though paying particular attention to timing methods may reveal reoccurring patterns that may be missed if the times are too widely spaced or at irregular intervals.

Modelbuilder

All of the methods discussed above are contained as geoprocessing tools in the ArcGIS Toolboxtoolbox. They can be combined in different combinations into a workflow model to solve specific spatial problems that answer geographic questions. Rather than manually repeating the steps of analysis over again each time a model is executed, they can be automated using ArcGIS' Modelbuilder application.

ModelBuilder is a graphical interface for diagramming and streamlining solutions to spatial analysis problems. You create Mmodels are created using ModelBuilder to chain together geoprocessing tools, using the output of one tool as the input to another tool. Creating a model automates a sequence of operations and data that results in the creation of new geographic information.

At the highest level, models contain only three things: elements, connectors, and text labels. Elements are the data and geoprocessing tools you work with. Connectors are the lines that connect data to tools. Text labels can be associated with the entire model, individual elements, or individual connectors. A tool plus its data is called a process. The output is the new geographic information created by the combination of tools with data.

Models enable repetitive reprocessing of data. As conditions change in a region, the model can be fed updated data at regular intervals to track that change. For example, as a storm tears through a region, updated flood levels delineated from high resolution imagery can be fed into an inundation model every two hours to track the extent of inundation over the land. A continuous flow of efficiently processed revised data in a situation such as this can greatly enhance decision support efforts in tactical search and rescue operations in times of crisisoperations that address power outages in regions undergoing catastrophic events.

Models can also enhance strategic planning efforts by feeding data sets of various hypothetical scenarios into a model to see the potential impacts of alternative policies and practices. Finding the best location for a wind turbine farm requires geographic and environmental analysis that includes multiple variables that can be computed, displayed and modeled with GIS. Analyzing the proximity to transmission lines and existing roads, and determining areas of moderate elevation with no wetlands, and no mature tree stands requires the use of extensive digital orthoimagery coupled with a tree inventory database to generate buffers and overlaid data layers. Using Modelbuilder to automate the steps in this analysis enables the analyst to repeatedly feed in data from various locations into the model until the most suitable site has been found.

MOBILITY: What's Going On In the Field

GIS helps manage data about the condition of utility assets. After parts of the system go into service, utilities must maintain the system through the collection and maintenance of asset condition data. Some condition data can come from automated systems and others from inspection systems. Utilities are rapidly adopting GIS-based mobile devices for inspection and maintenance. Enterprise GIS, with its desktop, server, and mobile components, allows utilities to gather condition data.

As stated earlier, the electric distribution system is old. Seasoned employees know this, but most companies have inadequate means of accurately capturing field information. Many utilities today still capture information from the field on paper. The paper must then be processed and validated in the office. A medium sized utility in Central America reported that it takes the utility over a year to process data from the field into their records system.

It's also difficult to manage the people in the field. A medium sized distribution company serving 1 million customers might have a thousand or more employees deployed in the field. Just keeping track of that many people and vehicles is an enormous task. The GIS (along with other corporate systems) helps route and organize the field force for better utilization

Utilities are complex. They have millions of pieces of equipment scattered over thousands of square miles of cities, towns, rural areas, remote fields, and mountains. Assets are strung on poles, buried in the ground, hidden in basements, and hanging high in the sky from huge towers. Usually the vast majority of employees and contractors are deployed to field locations. Utilities have line workers, meter readers, and troubleshooters. Utilities have much of their hard and human assets scattered in thousands of different directions. This dispersion of workers and assets is a challenge. Companies need to know where the workers are in relation to the assets. When a transformer catches fire, it is critical to know which assets are in close proximity to the transformer and where the nearest troubleshooter is to the burning transformer.

The key to a successful field operation is a solid knowledge of field worker location and activity. Of course, this is what GIS does best. Having access to mobile GIS in the field allows field crews to understand the location and the attributes of the assets. For example, GIS would tell a field worker the last time a device was maintained, it's rating, age, condition, manufacturer, failure history, and any other critical piece of information. GIS will tell the field worker about buried equipment or hard-to-access equipment. It will tell them about the land, the access locations are, the surrounding area, any sensitive habitat, and any incidence of crime or fire risk.

Likewise, field crews can provide information to the GIS, and to the office through the GIS. A field worker can fix data inaccuracies; attach pictures of damage to the asset record, and record inspection information. Organizations with many field workers and assets often have difficulty getting information from the field to the office. In a utility, for example, field workers will typically capture as-built information on paper field sketches. These sketches then have to be sent to the field office or corporate office. Since the sketches are manually prepared in a somewhat hostile environment they may not be clear enough for someone in a drafting office to understand or interpret. The time lag and the lack of clarity often results in a loss of accuracy and a lack timeliness of corporate data. Some utilities reportedly have field sketches that are more than a year old and have not been incorporated into the corporate data.

GIS helps utilities manage data about the condition of assets. After parts of the system go into service, utilities must maintain the system through the collection and maintenance of asset condition data. Some condition data can come from automated systems and others from inspection systems. Utilities are rapidly adopting GIS-based mobile devices for inspection and maintenance. Enterprise GIS, with its desktop, server, and mobile components, allows utilities to gather condition data.

How Mobile GIS WorksMobile GIS

Mobile users within an organization, such as a maintenance crew, require the ability to edit a portion of the ArcSDE GIS geodatabase in the field. This geodatabase data compilation workflow is referred to as Checkout/Check-in transactions, where they check out a portion of the database and disconnect completely from the organization's infrastructure, often for a prolonged period of time. When preparing for a particular work order or project, the relevant data would be replicated and transferred to a portable device such as a laptop or handheld mobile device. This device is then disconnected from the network, enabling the field crew to operate independently of the network. The field crew can continue to work with and modify the replicated data even though the crew is disconnected from the network. When a connection to the network is reestablished, any changes made to the data will be transferred back and synchronized with data maintained in the ArcSDE geodatabase.

Mobile Users

Additional mobile applications extend to those integrated with ArcGIS web applicationsServer to support a server pattern that allows geoservices to be extended into the field in a rich “sometimes connected” environment. ArcGIS Mobile GIS allows real-time data exchange that makes field-workers more efficient and connects their work in a near real-time environment, enabling more coordinated decision making. [http://www.esri.com/news/arcnews/winter0809articles/gis-geography-in-action.html

SITUATIONAL AWARENESS - Tell Me What's Going On Right Now

GIS allows utilities to visualize what they own and manage. If utilities have adopted smart grid or advanced metering infrastructure (AMI) or even extensive distribution automation equipment, their communications systems and the relationships that exist between them and the electric facilities have to be managed. Visualization goes well beyond the traditional "stare and compare" method commonly used by utilities to a notion of seeing relationships. GIS provides a means to monitor and express the health of the system in an obvious way

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