Geospatial Analysis Assignment Intro

Loading...

來自 University of California, Davis 的課程

Geospatial and Environmental Analysis

497 個評分

University of California, Davis

Geospatial and Environmental Analysis

497 個評分

課程 3（共 5 門，Specialization Geographic Information Systems (GIS)）

Apply your GIS knowledge in this course on geospatial analysis, focusing on analysis tools, 3D data, working with rasters, projections, and environment variables. Through all four weeks of this course, we'll work through a project together - something unique to this course - from project conception, through data retrieval, initial data management and processing, and finally to our analysis products. In this class you will learn the fundamentals of geospatial and environmental analysis during four week-long modules: Week 1: Tour ArcToolbox and learn how to use common geospatial analysis tools built into ArcGIS Week 2: Gain a working understanding of raster data models: symbolize, reproject, overlay, and assess rasters. Take a detour into 3D data models, and interpolation of observations into 3D surfaces and rasters Week 3: Go in-depth on projections and coordinate systems, which are foundational to all GIS. Learn how to use environment variables to constrain your analyses and get better quality data products. Week 4: Expand your knowledge of symbology. Learn how to visually display your data by classifying it in logical groupings and then symbolizing it on your map. Take Geospatial and Environmental Analysis as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first and second courses in this specialization, "Fundamentals of GIS" and "GIS Data Formats, Design, and Quality", before taking this course. By completing this third class in the Specialization you will gain the skills needed to succeed in the full program.

從本節課中

Course Overview & Geospatial Analysis

In the first module of Geospatial and Environmental Analysis, we will be learning about a plethora of common tools used with vector data in ArcGIS for geospatial analysis projects. By the end of the module, you will be able to use: the clip tool, erase tool, identity tool, near tool, point distance tool, merge tool, dissolve tool, tabulate area tool, buffers, and conversion tools. In addition, you will also begin work on a geospatial analysis project that we will be using throughout this course.

Geospatial Analysis Assignment Intro4:47

Data Acquisition for Our Project10:03

Getting the Crop Data5:23

Module 1 Summary1:40

與講師見面

Nick Santos
Geospatial Applications Researcher
Center for Watershed Sciences

[MUSIC]

Hello everyone and welcome back.

In this lesson, I'm going to introduce a project we'll be working on throughout

the rest of this course.

Each week, we'll go through a portion of the project together in the videos.

From project conception all the way through analysis.

If you've taken the previous courses, you've done some of this already, but

we haven't formalized it and walked through it together.

And we also haven't gone through the thought processes and

what you should be looking at during each phase of the process.

With this project, we'll do all of those steps together on video, but

then each week I'll give you some things to think about.

Whether it's how we might approach the problem, where to get the data, or

exactly how we might analyze it.

There won't be anything to turn in, or

even a tutorial to follow aside from the videos.

We're just trying to get you thinking about spacial analysis problems from

start to finish.

For those of you who want to think about and

work on these problems in earnest, you can participate using discussion questions.

We're structuring it as a problem we solve bit by bit each week for two reasons.

First, that's how problems are often solved in the real world.

You don't have the data given to you and

you have real problems that may not have a definite answer.

So, you need to be able to conceptualize the whole process start to finish and

then break it down into chunks that you can achieve right now.

The classic difficulty I see with new GIS analysts and

new programmers is trying to tackle a program all at once.

I fall victim to this pattern too, sometimes.

The more you can turn a problem into smaller tasks that are easier to

understand and troubleshoot, the better you'll be able to handle GIS projects.

Now, because it's how projects tend to actually work, it's also how our capstone

project is structured for those of you who are taking the whole specialization.

That project will be bigger and designed by each of you,

but we want to make sure you have practice building a project from the ground up

before you get to the capstone, so that you can be more successful there.

Okay, so, now that I've laid out the mechanics, let's talk about the project.

Since I haven't gone through this fully on my own yet, we'll truly be in it together.

I'd like to know what agriculture is occurring on floodplains in California?

For those of you who don't live in wet climates, floodplains are the natural

places that rivers spill out onto when too much water fills the river's main channel.

Growing crops in the flood plains presents opportunities, both for habitat and

nutrients, as well as economic hazards from disasters.

And this information would be useful to planners, researchers,

and emergency managers.

Again, since I haven't gone through this yet

I'm not positive about what data is available and what my end result is yet.

While I know that I ideally like a summary table of some sort,

maybe grouped by county, or crop type, or region, and some resulting maps,

I won't settle on an end product until I really know what data is available for

me to start with sometimes you know where you need to end up, and

have to work backward instead.

But, in this case, we'll aim to summarize crop types in flood planes and

we'll figure out what options we have once we see the data available and

can plan our analysis.

My method is certainly not the only way to go about this.

If you have your own way of approaching problems and

feel successful with it, use it.

This project isn't meant to be fully prescriptive.

Instead, it's mean to help those of you with less experience in this area

get some practice and

aid in the process of breaking down a problem into smaller pieces.

In this case, in my approach, the first piece is to survey the available data.

Now that we have our project laid out, let's figure out what's next for

each of us.

Next up for you is to think about what data we might seek out

to solve this problem.

Consider not just what data, but why it's potentially useful for the analysis.

Consider what the bare minimum is,

but also what your ideal data might look like so you can seek that out too,

whilst still knowing when you've met the basic needs of the project.

Also think about where we might be able to obtain this data.

What organizations or governments create this data.

Will it be structured already into formats you're familiar with?

Or will we need to generate it ourselves through digitizing or

some other methods based upon some other source dataset.

So think about these things for a little while before you proceed.

The next item in the course is a discussion question.

Chime in there and

engage your classmates in what you think we're all going to look for.

Then we'll come back together once more this week in a video,

where I'll continue on, and search for the data with you.

After that, we'll leave you to consider what we can with the data until next week,

when we'll pick up the project again.

OKay, think about what data we might want for

now and jump into the discussion coming up next.

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。

Coursera 致力於普及全世界最好的教育，它與全球一流大學和機構合作提供在線課程。

© 2018 Coursera Inc. 保留所有權利。

通過 App Store 下載

通過 Google Play 獲取