Mitchell Chapter 5.5

VORTEXII

Another beautiful day in the neighborhood

Selecting Features Inside An Area:

Using this method you basically specify the features and the area desired.

GIS will then check the location and flag the features that are within the area selected.

You can then use a data table to get further information about the features which allows you to summarize attributes associated with them.

This information can be used in several ways:

Examples:

PARCELS

Parcels within a 100-year floodplain.

911calls

Calls to 911 within a group of neighborhoods. In this method it does not distinguish between features, it only shows that it is one of them.

LIQ

Parcels within 500 feet of a restaurant requesting a liquor license.  This example shows how features can be used to see a geological selection with features in a certain distance.

students

Number of student by census block group.  This example highlights data that is already summarized by an area and fully enclosed within its boundaries.

Using the results:

Using GIS to create a report you can then use the information as needed. For our project this could be used to show all the trees that would be in a possible restoration path.

Example: Properties that would be  within 500 feet of a proposed liquor store:

information

There are several statistical summaries that can be used as well:

Count: total number of features inside the area.

Frequency: number of features with a given value or with a range of values inside a given area. This can be displayed as a bar chart or pie chart for easy reference.

Example:

charts

A summary of a numeric attribute:

Based on:

*Sum: an overall total.

*Average (or mean): total of numeric attributes divided by number of features. (reminder that very high/low numbers can skew the average)

*Median: middle range of values.

*Standard Deviation: average amount values are from mean.

Basic concept is that you would show features inside desired area with different colors to highlight the areas of interest. This can be with single attributes (like 100 year flood) or multiple attributes like property types.

Example:

100yr property

Overlaying Areas and Features:

This method allows one to find discrete features inside the selected area and summarize them.

Overlaying areas with discrete features:

This tool is versatile because the attributes are permanently stored in the features data table. With GIS tags each feature has a code for the area it falls within and then assigns the areas attributes to each feature.

In short this allows to overlay different sets of information to get an idea of how they interact.

Example of how this works: pg. 107

Overlay example

*Something to think about when creating a map this way. To show the area that the information is representing think about using brighter colors to show the mapped area surrounded by neutral colors. (for example a floodplain would be more visible than the whole parcel affected by it)

Overlaying Areas with Continuous Categories or Classes:

Much like the aforementioned this uses GIS to summarize the amount of each class or category falling within one of more areas. A good example would be precipitation mapping.

This method uses either Vector or Raster method:

Due to the way that the vector method works there is a chance that you end up with areas called “Slivers”, this is where borders become slightly offset.

GIS provides a tool to automate the process  of merging subsequent calculations into larger adjacent areas.

Important reminder about Raster and Vector methods:

Vector is more a more precise measure of areal extent but can cause more processing due to removal of slivers.

The Raster method is more efficient but can be less accurate.

Examples of results:  (left: single area with multiple categories, right: Multiple areas with single category, not shown multiple areas with multiple categories)

single area with single results multiple area with single results

Overlaying Areas With Continuous Values:

basically if you have an area like elevation GIS can summarize the statistic in each area based on things like mean, minimum value, value range, standard deviation, and sum.

 

 

 

 

 

 

 

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