GIS for Environmental Science ENSC 3603 Class 17 3/10/09

GIS Implementation Phases Assessment and evaluation of the current situation; Development of a business concept; Identification and specification of user requirements; Identification and acquisition of data; Cost-benefit analysis; Devising a strategic plan; Choice of hardware and software; Defining and obtaining the necessary expertise; Choosing a GIS supplier; System implementation; Operation and maintenance of the system (Bernhardsen, 1999)

GIS Implementation Phases Assessment and evaluation of the current situation; –Where are we now and what do we have to work with. Development of a business concept –What is the organization’s purpose and what approach does it use to carry it out. –Forms the basis for defining tasks to be implemented by the organization. (Bernhardsen, 1999)

GIS Implementation Phases Identification and specification of user requirements; –Identified through user surveys, workshops, and a review of documented experience. –This should lead to a list of user needs.

GIS Implementation Phases Identification and acquisition of data ; –Data quality, user needs, costs, accessibility and available time frame. –Data is chosen only after user tasks and needs have been identified and analyzed. –Data model – data must be organized before it is useful. –Data Inventory – –Data Quality – Data accuracy depends on the quality of the original input data and the precision of the data processing Georeferencing accuracy Attribute data accuracy Logical relationship consistency Data completeness and data resolution Data currency

Data Quality: Data quality is defined as: –The “fitness for use” of data for intended applications. –When GIS began to be widely used in various applications in 1990s, data quality emerged as an important topic of the data component of GIS. (Lo Chapter 4, and notes from S. Shaw)

Why is data quality an important topic in GIS? –Informal assessment of data quality and allowance for inaccuracies, break down when we move from manual methods to GIS. –Implicit assumptions about data quality must be made explicitly in GIS (e.g., topology). –Errors can propagate through GIS data processing steps (e.g., map projection, map overlay). –One advantage of GIS is to integrate diverse data sets. However, data may be used in ways not foreseen by their producers and by users without the knowledge or experience to judge whether an application is appropriate.

An example of Error Propagation

Should we always look for the highest possible data quality level? –No. Demanding a higher level of data quality than actually needed quickly becomes a significant and unnecessary expense. –The “level of data quality” should be balanced against the “cost of the consequences of less accurate data”. –One reasonable choice of data quality level is to go for “the minimum level of data quality that will meet your needs.”

Four Generic Measures of Data Quality: Accuracy. –is defined as “the degree to which data agree with the values of the real-world features that they represent”. –In other words, accuracy is a measure of “how close data match the true values.” –In practice, some true values can never be exactly determined due to the limitations of instruments or human inability to perform perfect observations. –Accuracy is application-specific. It is imperative to collect data at a level of accuracy commensurate with the objectives of the intended applications. –Two types of accuracy measures: positional accuracy and attribute accuracy

Precision. –Precision is a measure of “how exact data are measured and stored.” –For numerical data, it is usually represented by “the number of significant digits used to record the data.” For example, using 32-bit integers, it is possible to create a global database at a precision of approximately 2 centimeters (cm). –For categorical data, the measure of precision is based on “the level of detail” (e.g., number of classes) to which the data are observed and recorded. –High precision does not necessarily mean high accuracy.

Error. –Error is closely related to accuracy (i.e., Higher accuracy implies fewer errors). –In a broad sense, error also includes the concept of “deviation”, “variation” and “discrepancy” in statistics. –Three types of errors: Gross errors – refer to “mistakes”. They can be detected and avoided via well-designed and careful data collection. Systematic errors – occur due to factors such as human bias, poorly calibrated instruments, or environmental conditions. Random errors – They cannot be avoided and can be treated with mathematical/statistical models.

Uncertainty. –Uncertainty and error are often used as synonyms in GIS. However, there is one basic difference between them. –While “error” refers to the lack of accuracy and precision in the data, “uncertainty” implies the lack of confidence in the use of the data due to incomplete knowledge of the data. –In other words, uncertainty is a measure of “what we do not know about the data”.

Sources of Error in Geographic Data Inherent errors –Occur as a result of the special nature of geographic data, as representations of the real world in a certain data model, are incomplete and generalized. The real world is very complex. Operational errors –Occur during the operation of the procedures for collecting, managing, and using geographic data.

Sources of Error in Geographic Data: Inherent errors and operational errors

Components of Geographic Data Quality: The U.S. National Committee for Digital Cartographic Data Standards (NCDCDS) identified five data quality components: –Lineage –Positional accuracy –Attribute accuracy –Logical consistency –Completeness

1.Lineage. The lineage of a data set refers to "its history, the source data and processing steps used to produce it.” Lineage information often exists as personal knowledge of a few staff members and is not readily available to most users. Even though it is available, it's usually in the form of procedure manuals or internal documents, which are not usually provided with the data set. Lineage information should be included with the data set so that it is readily available to users.

2.Positional Accuracy. defined as “the expected deviance in the geographic location of an object in the data set from its true ground position.” usually tested by comparing a set of sample points with an independent and more accurate source of information. National Map Accuracy Standard

3.Attribute Accuracy. defined as “the closeness of the descriptive data to the true or assumed values of the real-world features.” Attributes may be “metric" or “categorical" variables. Assessing accuracy of “metric" variable is similar to that for positional accuracy. Assessing accuracy of “categorical" variable is more troublesome due to factors such as: –sharp boundaries often do not actually exist in the field –the appropriate number of classes to use

4.Logical Consistency. is “a description of the fidelity (Exact correspondence with fact or with a given quality, condition, or event; accuracy) of the relationships between the real-world and encoded geographic data." For example, data consistency among different GIS data layers (e.g., Rivers are also county boundary lines). Otherwise, when we overlay these GIS layers, they could introduce problems of logical consistency (e.g., “sliver polygons”). There is no standard measure of logical consistency. It is best addressed before data are entered into GIS.

5.Completeness. refers to “the degree to which the data exhaust the universe of all possible items.” Two types of completeness: –Spatial Completeness: refers to the proportion of data available for the area of interest. –Thematic Completeness: refers to whether the data contain all the layers required for the intended applications. It also refers to the completeness of the chosen classification system to represent the data.

–Spatial autocorrelation

Managing Data Error: There exist errors with all geographic information. –Errors are introduced at every step in the process of producing and using geographic information. –No map can be 100% accurate. –The objective in dealing with errors should not be to eliminate them but to "MANAGE" them. –Achieving the lowest possible level of error may not be the most cost-effective approach. QA/QC (Quality Assurance and Quality Control) –Someone needs to watch for these data quality issues.

Geographic Data Standards: Types of standards: –de facto standards (also known as industry standards): standards that gain wide acceptance through popular use rather than through a formal standard development process –de jure standards: standards developed by an organization empowered to create standards (e.g., ANSI) –regulatory standards: They are usually de jure standards that have been established by government legislation and have certain legal power behind their enforcement.

Components of Geographic Data Standards: Standard Data Products: USGS - Digital Line Graph (DLG) Digital Elevation Models (DEM) Digital Orthophoto Quadrangles (DOQ) Digital Raster Graphics (DRG) US Census Bureau - TIGER Data Transfer Standards: USA - Spatial Data Transfer Standard (SDTS) Canada - Canadian Geomatics Interchange Standard (CGIS) United Kingdom - National Transfer Standard (NTS)

Components of Geographic Data Standards: (cont.) Data Quality Standards: –USA - National Map Accuracy Standard Metadata Standards: –USA - Contents Standard for Digital Geospatial Metadata

Spatial Data Standard - SDSFIE Spatial Data Standards for Facilities, Infrastructure, and Environment (SDSFIE) SDSFIE Browser ver. 2.5 – software used to help convert data to the SDSFIE standard.

Spatial Data Standard - SDSFIE SDSIFE Entity sets- Auditory Boundary Building Cadastre Climate Common Communications Cultural Demographics Ecology Enviromental_Hazards Fauna Flora Future_projects Geodetic Geology Hydrography Improvement Land_status Landform Military_operations Olfactory Soil Transpiration Utilities Visual

Spatial Data Standard – SDSFIE Selected Entity sets, Classes, Types, Tables, Attributes and data examples Entity SetEntity ClassEntity Type (Feature Class) TableAttributesExample or domain buildingsbuilding_spacebuilding_floor_ area bgspaspadatalink buildng_id address_id agency_id Cassatt St hydrographyhydrography_s urface surface_water _body_area hysurwbdsur_bod_id body_name body_desc body_typ_d area_size area_u_d year 1 POND RESERVOIR M_RESERVOIR 5 ACR 1963? land_statusland_status_la nd_condition land_use_arealscndluslanduse_id use_name use_typ_d area_size area_u_d 1 SOYBEANS FARM_CROPS 2.3 ACR

Read and Do Read Chapter 4 in Lo ESRI Module 8 due this Thursday

"Times of great calamity and confusion have been productive for the greatest minds. The purest ore is produced from the hottest furnace. The brightest thunderbolt is elicited from the darkest storm." - Charles Caleb Colton ( ), writer "Nothing in all the world is more dangerous than sincere ignorance and conscientious stupidity." - Martin Luther King Jr.