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:
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
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
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!
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).
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!
First let's consider the VECTOR operations - there are many different Spatial Analyses you can run...namely:
Summarize Data
Find Locations
Data Enrichment
Analyze Patterns
Use Proximity - what is near what?
Manage Data - organizing and combining data
Now let's consider the RASTER operations - there are many different Raster Analyses you can run...namely:
Summarize Data -
Analyze Patters
Use Proximity
Analyze Image
Analyze Terrain
Manage Data
Deep Learning
Multidimensional Analysis