Data Representation in GIS

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En provenance du cours de University of California, Davis

Fundamentals of GIS

2071 notes

University of California, Davis

Fundamentals of GIS

2071 notes

Cours 1 sur 5 dans Specialization Geographic Information Systems (GIS)

Explore the world of spatial analysis and cartography with geographic information systems (GIS). In this class you will learn the basics of the industry’s leading software tool, ArcGIS, during four week-long modules: Week 1: Learn how GIS grew from paper maps to the globally integrated electronic software packages of today. You will install ArcGIS on your computer and learn how to use online help to answer technical questions. Week 2: Open up ArcGIS and explore data using ArcMap. Learn the foundational concepts of GIS, how to analyze data, and make your first map. Week 3: Make your own maps! Symbolize data and create an eye-catching final product. Week 4: Share your data and maps and learn to store and organize your data. Take Fundamentals of GIS as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. By completing the first class in the Specialization you will gain the skills needed to succeed in the full program. Students who need an ArcGIS license will receive a non-commercial, 1 year student license for participation in this course and specialization.

À partir de la leçon

Course Introduction and Introduction to Geographic Information Systems (GIS)

In this module, we will cover course expectations, give you a quick overview of GIS and what's great about it, take a first look at ArcGIS and identify key elements in the interface, and define core geospatial concepts and terminology. In Section 2, we will discuss options for desktop GIS, the history of GIS and how it's used today, discuss resources and help that you can use, and lay out core skills that are relevant to you as a GIS analyst. We'll close out by showing you how to get a copy of ArcGIS for this course, and with a tutorial on getting started in ArcGIS.

Module 1 Overview1:07

Why GIS is Awesome6:51

What is GIS?12:22

A First Look at Using Desktop GIS8:56

GIS Terminology to Know9:34

Tour of ArcMap14:43

Data Representation in GIS7:05

Rencontrer les enseignants

Nick Santos
Geospatial Applications Researcher
Center for Watershed Sciences

[MUSIC]

Hello, again and welcome back.

In this lesson, we're going to talk about how GIS data is structured and

what that means for how you collect and process data.

To understand data types, we're going to take this scene I drew and

look at how it might translate into geospatial data.

This is just a basic sketch and I'm no artist, but pretend for a moment that this

is a real world scene of a river in blue with vegetation on both sides in green and

patches of dirt with no grass or vegetation in the tan color.

So now, the central question is how do we conceptually translate this location into

geospatial data?

Before dive in, I want you to think about data collection for a moment.

In some way, data collection is always in an approximation of the real world.

We can't capture everything about a location in our data, so we discard

the information that's not important to our particular application of that data

and collect the things that are meaningful to us whether it's how it looks or

what it does.

For example, think about the contacts manager application in your phone or

in your Rolodex for some of you.

The information you store about a person isn't a complete description of them.

It's things like a phone number, an email address, maybe their physical address,

maybe some other notes.

As a profile of a person, it's an approximation.

It's not sufficient to recreate a person by any means, but for

certain uses it's exactly what we need.

GIS data is the same.

We simplify the world into data structures we can use in our work.

There are many types of data, but we'll talk about four of them right now.

Rasters and then the vector data types of points, lines and polygons.

Remember that points are dimensionless, just a location in space and the size of

them is purely based on how we choose to symbolize or display them on our map.

Lines are one-dimensional in principle, but two-dimensional once you

start collecting multiple segments to form things like roads and

then polygons cover an area and are typically used to group locations that

we're classifying as being of the same type.

Overall, vector data is great for discrete observations.

The main alternative to the vector data types is to use a raster, instead.

Remember that these are grids like a chess board, where each cell or

pixel can have a value.

If we want to cover more area,

we can't just add more features like we can with vector data.

We need to add more rows or columns to the raster and indicate values even if

it's a null or unknown value for everything in those rows and columns.

These are the best bet for

use cases where your data needs to vary continuously across the landscape.

So thinking again about the scene we were just looking at.

If we take it to be what reality looks like,

how do we approximate it as GIS data?

The first choice is what type of GIS features to use?

Probably, the simplest way that we can turn this scene into the GIS data is just

marking out points of features of interest.

I can start by simply creating points where the dirt patches are located.

This gives me a simple representation of the scene.

We don't have the grass locations or

the river, we could potentially turn those into points two, but

that doesn't make as much sense as turning these dirt patches into points.

Again, a reminder that this is an approximation of our data.

What we create here isn't going to give us the exact dirt patches, but

it shows us the locations of the patches and

we can use data attributes, which will cover more in an upcoming lecture

to help us characterize them in other ways that we can measure.

For the river, I am more likely to want to turn that into a line instead of points.

That makes a lot more sense for something like a river or roads.

This line is still an approximation of the river's location.

We don't get width information for this locations.

But again, we can add attributes that give us specific information

about the river that we can observe it an other way.

The location itself is best represented by a line and

what if I want to characterize the land cover in general?

In this case, maybe I would want to use polygons.

I can draw polygons representing the boundaries of these dirt patches and

at that point, I have area information for them and

can see how they relate to everything else a little better.

I can also draw the boundaries of the vegetation areas, which happen to surround

the dirt patches, but there will be holes in the vegetation polygons for them.

And then if I want to, I can draw out the river as well.

When I'm done, I can just draw letters in to visualize the attributes that I might

put on these polygons.

In a real world use case, I would most likely do polygons or points for

the dirt patches.

Polygons for the vegetation, depending upon the type of the vegetation and

then a line for the river.

What about if I want to use a raster format instead?

In fact, some lane covered datasets do use rasters.

To start with, let's draw out a bare bones grid, so we can visualize our raster.

These lines don't actually exist in our dataset, but

for us to image a raster, it helps to see them.

Also raster cells, the squares here are usually of equal width and height, so

ignore the not uniformity in my drawing here.

Remember that a key component of a raster dataset is that every location within

a cell has the same value, because each cell codes for just one value.

This is easy for the first few items, which we can say,

consist almost entirely of grass and I'll draw that in here.

But what about for the cells that have mixed portions of dirt and

grass and river?

These mixed pixels can be assigned values by a couple of different rules.

We can either assign by the majority value or

by what value is in the center of the pixel.

In this case, I'm going to approximate assigning values by majority and

write in g for Grass, D for

dirt, W for water based on what I think it looks like the value would be.

When we're done,

what we would have is cells with single colors representing their uniform values.

Our raster would most likely be stored with integer values like one, two, three.

Coding for these different land cover types, but

here I've represented them with the letters instead.

So again, let's think of the inaccuracies we introduced in our translation of our

real world data into a GIS dataset.

If the values of these raster cells are uniform, then we lose a ton of precision

about the world here when we assign these mixed pixels to a single value.

This is inherent to taking data.

The best way to minimize it is by choosing the data format that makes the most sense

for the place you are working with and the analysis you intend to do.

We'll go through these concerns a little more in another course in

the specialization, but it's something to keep in mind for now.

That's it for this lesson.

I hope you have a better understanding of the different GIS data types and

when you would choose to use them.

See you in the next lecture.

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