Being my first blog post ever, I wanted to cover a topic that is near and dear to my heart. I want to target this post to both individuals who know GIS, as well as those that do not (for those of you who do, you can skip to Part 2: Development Methodologies [Coming Soon] if you would like).
What is GIS?
According to WikiPedia – A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data.
However, more recently, GIS has become an overarching term that can be used to not only describe in high-level what spatial science is, but also describe the entire industry and ecosystem of spatial science as a whole.
My personal definition is as follows:
Geographic Information Systems (or GIS) are the technological product of spatial sciences. In a basic sense, they are information systems or applications used to present and analyze both geographical and non-geographical information in a geographical format.
Please note that I am using modern-day usage of the term to define GIS. In all actuality, a GIS can embody older methods of analyzing the aforementioned information. However, as of the time of writing this post, software systems are the primary medium in which GIS systems are presented.
Breaking it down: The components of a GIS
So what are the main components of a Geographic Information System? In the GIS industry, there are many debates as to what exact features need to be included in a mapping system to be considered “GIS”. We have many commercial, or what I would call, “average-user” based mapping systems such as Google and Bing maps. The difficult question to answer is, are these actually GIS?
This is a pretty large topic, and there are a lot of opinions going either way.
Regardless what the community decides the answer the this is, it can give us a good starting point for breaking down the components of a GIS system. Lets start with the basics:
A map is necessary for understanding the environment, surrounding terrain, and overall location of the data or area you are observing.
Layers on a map are the core of representation of GIS data. They are collection containers for features of a certain feature type. Standardly, GIS software allows you to turn these on and off (show or hide).
Features are the items contained within a layer. You can see below that there are three layers (schools, parks, roads) that have a certain number of features inside of them. You can also see that each layer has a feature type (points, polygons, lines).
Layers & features both have constraints that are set on them to tell the software what kind of feature type they are or contain. Some examples of feature types are Points, Polygons, Lines, and Annotations.
Now at this point, based on the information we have, we could easily say that Google Maps is a GIS! Right? Well… maybe. It has a map, layers (traffic, bicycling, terrain), and features with types (lines for traffic and bicycling, points for restaurant and public areas). However, what it does not have that is pertinent to GIS (looking back to our definition) is analysis tools. Analytics is the difference between consumer mapping products and GIS.
Now that we have looked at some of the basics of GIS, let’s get into the meat of what makes GIS truly what it is.
Spatial analysis is the practice of using geographical and supplementary information to form a model or hypothesis as a basis for discussion or interpretation.
There is a very large handful of spatial analysis actions we can take to help understand our data more efficiently, and to be confident with our assumptions and calculations. However the most common spatial analytic processes we entertain are:
- Measurement Analysis – The action of measuring the distance, perimeters, or areas of a layer, feature, or map.
- Layer Statistics – Statistics generation such as ratios, histograms, trend analysis, and semivariograms based on the attribute(s) of a layer or feature(s).
- Queries – Either tabular (SQL-like) or spatial queries based on the supplemental information or spatial data of a layer or feature(s) (respectively). Adjacency and radius are examples of a spatial queries (based on spatial information), while standard SELECT queries can also be performed based on supplemental information (layer or feature attributes).
- Buffering – The action of creating a buffer around a feature (point, polygon, or line) to be able to exemplify distance around that object.
- Proximity – The action of creating concentric equidistant zones established around a starting point. Also used to measure distance.
- Feature-In Spatial Queries – The action of using particular methods to find if features are either inside or overlap one another.
- Transformation – Functions that transform a layer of one feature type to another, and do Raster-Vector conversion.
- And many more…
You get the drift. You can’t do these things in Google Maps nor should you try. Let’s throw consumer-based or Neogeography away. From Wikipedia:
Neogeography (literally “new geography”) is the use of geographical techniques and tools for personal and community activities or by a non-expert group of users. Application domains of neogeography are typically not formal or analytical.
In this series, we are going to turn you into the expert users (or at least beginning expert users, but great GIS developers) that use mapping applications for analytical reasons. Neogeography will be used from this point forward to describe consumer-based mapping applications for use in comparison only.
Goodbye Google Maps, hello science!