Attribute based and Spatial Operations Section III Part 1: Attribute Based Operations

Notes about ArcView Project Files –a project is the highest organizational structure in ArcView. It allows you to group all program components-views, theme, etc, into a single unit. –ArcMap employs a map document file.mxd –ArcView stores a project in a project ASCII file.apr, which stores the ArcView environment exactly as it was set up the last time you saved the project. –Project files are dynamic. It stores the steps that leads to the views of the data are stored rather than the data itself. The project remains current with the data. When you open a project, database operations, such as table joins or logical queries, will be performed anew. –ExampleExample

I- Attribute-Based Operations Operations that are not based on spatial locations. Usually a step towards further analysis A- Reducing the information content: Operations performed to reduce the amount of details in the source data such as: 1- Grouping (dissolving) This procedure produces a cruder classification by merging some of the classes. For example, a detailed map of roads is classified into SR or non-SR.

Lease ID areas with stands polygo ns within Polygons dissolved into the lease ID areas Chapter 11

2- Isolating A selection process that can be based on attributes. Divides the data into selected and not selected, isolate a selection. For example, select the areas of certain slope from a soil coverage.

Polygon F is isolated through a selection process Chapter 11

3- Classification Data are classified into a set of classes. Class intervals establish the breakpoint between adjacent categories. Examples: equal intervals: the total range of attribute values is divided by the number of classes. Breakpoints are spaced at equal intervals, which may result in empty classes. Suitable for data that is uniformly distributed over the total range. quantiles: an equal number of objects is assigned into each class. Quantiles are best suited for data that is linearly distributed.

natural Breaks: the default classification method in ArcView. This method identifies breakpoints between classes using a statistical formula (Jenk’s optimization). This method is rather complex, but basically Jenk’s method minimizes the sum of the variance within each of the classes. Natural Breaks finds groupings and patterns inherent in your data. Equal Area: This method classifies polygon features by finding breakpoints so that the total area of the polygons in each class is approximately the same. Suitable for studies based on areas.

Standard Deviations: the software finds the mean value and then places class breaks above and below the mean at intervals of either 1/4, 1/2, or 1 standard deviations until all the data values are contained within the classes. ArcView will aggregate any values that are beyond two or three standard deviations from the mean into two classes, greater than two or three standard deviations above the mean ("> 3 Std Dev.") and less than three standard deviations below the mean ("< -3 Std. Dev."). Suitable for data that is randomly distributed.

Countries in Africa are classified according to the population in 2000, natural breaks One class, the largest population, includes only one country.

Countries in Africa are classified according to the population in 2000, Equal intervals at 5 classes. Their might be a class with no features!

Countries in Africa are classified according to the population in 2000, Quantile, 5 classes.

Countries in Africa are classified according to the population in 2000, Standard deviation 4 classes at one standard deviation breaks.

B- Increasing the Information Content Requires some external source of information 1- Rank: polygons in a soil type theme can be ranked into slight, moderate, or severe according to suitability for recreational development. You need to combine additional data about the soils. 2- Evaluate: vegetation classes might be assigned the expected density of a species of reptiles. You give numbers to vegetation areas based on certain evaluation. 3- Rescale: a continuos measurement can be rescaled using a mathematical function to represent some other property. For example, converting the temperature through a non linear equation into the biomass produced by a tree.

C- Combining Pairs of Input Values When operations involve more than one attribute. 1- Cross-tabulate: a new attribute is created based on the combination of two attributes. For example, the PH level in lakes and depth, high PH in deep water, low PH in deep water, etc. 2- Mathematically produced attributes: a new attribute is created by applying mathematical operators such as difference and rate on other attributes: increase of population in 10 years 3- Performing combinations: Joining tables in a relational database using a foreign key (common field) expands the attribute table.