Spatial & Raster Data & Analyses

General Info

Appropriate Expectations...

• This is the ONLY GIS course offered at Goshen College → we can’t possibly adequately cover even the basics of GIS, that would take at least a 3 course series

• Instead, think about this as your taste of GIS and introduction to spatial thinking

• We will get experience with major GIS apps, but will not go in depth into any of them

• You will get a taste of both mapping and analyses...but there will still be plenty to do in both!

• The point of this course is to learn how to think geospatially...not to learn a specific program

• It is my hope that you can take the basic concepts you learn in this class and apply them to your life...wherever it leads!

• The world of geospatial analysis is so wide and deep...I can’t possibly know how to do everything...we are learning together!  I try my best to work backwards and debug but it is likely that many issues will elude me

• This is fun!  Mapping is for everyone!  You will come back to these skills again and again.

Here is the basic aim of this course:

1. Data Ingestion

   • Look for and find data, or collect new data

   • Process the data so it is clean

   • Figure out which software to use

   • Figure out how to get your data into your software

2. Analysis

   • Explore your data visually, with summary statistics, and with spatial statistics...look for trends and interesting relationships to test

   • Perform appropriate statistical analyses to find the geospatial meaning of your data

3. Solutions & Visualizations

   • Figure out what to do with the results of your analysis...what are the implications?

   • Create crisp and clear visualizations that help your client see and understand the implications of the data....learn how to communicate!

Attribute Tables

Non-spatial information associated with a spatial feature is referred to as an attribute. A feature on a GIS map is linked to its record in the attribute table by a unique numerical identifier (ID). Every feature in a layer has an identifier. It is important to understand the one-to-one or many-to-one relationship between feature, and attribute record. Because features on the map are linked to their records in the table, many GIS software will allow you to click on a map feature and see its related attributes in the table.

Raster data can also have attributes only if pixels are represented using a small set of unique integer values. Raster datasets that contain attribute tables typically have cell values that represent or define a class, group, category, or membership. NOTE: not all GIS raster data formats can store attribute information; in fact most raster datasets you will work with in this course will not have attribute tables.

Measurement Levels

Attribute data can be broken down into four measurement levels:

• Nominal data which have no implied order, size or quantitative information (e.g. paved and unpaved roads)

• Ordinal data have an implied order (e.g. ranked scores), however, we cannot quantify the difference since a linear scale is not implied.

• Interval data are numeric and have a linear scale, however they do not have a true zero and can therefore not be used to measure relative magnitudes. For example, one cannot say that 60°F is twice as warm as 30°F since when presented in degrees °C the temperature values are 15.5°C and -1.1°C respectively (and 15.5 is clearly not twice as big as -1.1).

• Ratio scale data are interval data with a true zero such as monetary value (e.g. $1, $20, $100).

Exploratory Analyses ...

Below are the analyses available in ArcGIS Online.  Read the summary webpages for Spatial Analyses & Raster Analyses.  You don't have to go through all the links below, they are there for your reference and to show you how much you can do!

Vector Analyses (ArcGIS Online)

1. • First let's consider the VECTOR operations - there are many different Spatial Analyses you can run...namely:

     1. 1. • Summarize Data 

             1. • Aggregate Points

                • Join Features

                • Summarize Nearby

                • Summarize Within

                • Summarize Center and Dispersion

           • Find Locations

             1. • Find Existing Locations

                • Derive New Locations

                • Find Similar Locations

                • Choose Best Facilities

                • Create Viewshed

                • Create Watersheds

                • Trace Downstream

                • Find Centroids

           • Data Enrichment

             1. • Enrich Layer

           • Analyze Patterns

             1. • Calculate Density

                • Find Hot Spots

                • Find Outliers

                • Find Point Clusters

                • Interpolate Points

           • Use Proximity - what is near what?

             1. • Create Buffers

                • Create Drive-Time Areas

                • Find Nearest

                • Plan Routes

                • Connect Origins to Destinations

           • Manage Data - organizing and combining data

             1. • Extract Data

                • Dissolve Boundaries

                • Generate Tessellations

                • Merge Layers

                • Overlay Layers

Raster Analyses (ArcGIS Online)

1. • Now let's consider the RASTER operations - there are many different Raster Analyses you can run...namely:

     1. 1. • Summarize Data - 

             1. • Summarize Raster Within

                • Zonal Statistics as Table

           • Analyze Patters

             1. • Calculate Density

                • Interpolate Points

           • Use Proximity

             1. • Distance Accumulation

                • Distance Allocation

                • Optimal Path As Line

                • Optimal Path As Raster

                • Optimal Region Connections

           • Analyze Image

             1. • Monitor Vegetation

           • Analyze Terrain

             1. • Calculate Slope

                • Create Viewshed

                • Derive Aspect

                • Watershed

           • Manage Data

             1. • Convert Feature To Raster

                • Convert Raster To Feature

                • Extract Raster

                • Remap Values

                • Sample

           • Deep Learning

             1. • Classify Objects Using Deep Learning

                • Classify Pixels Using Deep Learning

                • Detect Objects Using Deep Learning

           • Multidimensional Analysis

             1. • Aggregate Multidimensional Raster

                • Find Argument Statistics

                • Generate Multidimensional Anomaly

                • Generate Trend Raster

                • Predict Using Trend Raster