Spatial data is special and can be problematic. However, as we will see later in this course, there are instances when this can be useful, so understanding how we deal with it is important.

Figure 1.0. Modifiable Areal Unit Problem

Credit: Blanford, © Penn State University, licensed under CC BY-NC-SA 4.0 [1]

Learning Outcomes

By the end of this lesson, you should be able to:

• List four major problems in the statistical analysis of geographic information: autocorrelation, the modifiable areal unit problem, scale dependence, edge effects.
• Identify and discuss the implications of the Modifiable Areal Unit Problem.

Checklist

Lesson 1 is one week in length. (See the Calendar in Canvas for specific due dates.) The following items must be completed by the end of the week. You may find it useful to print this page out first so that you can follow along with the directions.

Questions?

Please use the 'Discussion Forum' to ask for clarification on any of these concepts and ideas. Hopefully, some of your classmates will be able to help with answering your questions, and I will also provide further commentary where appropriate.

Spatial Autocorrelation

The source of all the problems with applying conventional statistical methods to spatial data is spatial autocorrelation [3]. This is a big word for a very obvious phenomenon: things that are near each other tend to be more related than things that are far apart. If this were not true, the world would be a very strange and rather scary place. For example, if land elevation were not spatially autocorrelated, huge cliffs would be everywhere. Turning the next corner, we would be as likely to face a 1000-meter cliff (up or down, take your pick!), as a piece of ground just a little higher or a little lower than where we are now. An uncorrelated, or random, landscape would be extremely disorienting.

The problem this creates for statistical analysis is that much of statistical theory is based on samples of independent observations that are not dependent on one another in any way. In geography, once we pick a study area, we are immediately dealing with a set of observations that are interrelated in all sorts of ways (in fact, that's what we are interested in understanding more about).

Having identified the problem, what can we do about it? Depending on how deeply you want to go into it, quite a lot. At the level of this course, we don't go much beyond acknowledging the problem and developing some methods for assessing the degree of autocorrelation (Lesson 4). Having said that, there are some methods that recognize the problem and take advantage of the presence of spatial autocorrelation to improve analysis. These include point pattern analysis (Lesson 3) as well as interpolation and some related methods (Lesson 6) that recognize the problem and even take advantage of the presence of spatial autocorrelation to improve the analysis.

The Ecological Fallacy

The ecological fallacy may seem obvious, but it is routinely ignored. It is always worth keeping in mind that statistical relations are meaningless unless you can explain them. Until you can develop a plausible explanation for a statistical relationship, it is unsafe to assume that it is anything more than a coincidence. Of course, as more and more statistical evidence accumulates, the urgency of finding an explanation increases, so statistics remain useful.

In Lesson 1's reading we learned about some of the reasons why spatial data is special, including spatial autocorrelation, spatial dependence, spatial scale, and the ecological fallacy.

This week in our project we will look closely at another pitfall, the Modifiable Areal Unit Problem (MAUP).

The Modifiable Areal Unit Problem (MAUP)

Often, MAUP is considered to consist of two separate effects:

1. A shape or zonation effect
2. A scale or aggregation effect

Both effects are evident in the example in Figure 1.2 and further emphasized in Figure 1.3. The shape effect refers to the difference that may be observed in a statistic as a result of different zoning schemes at the same geographic scale. This is the difference between the 'north-south' and 'east-west' schemes. The scale or aggregation effect is observable in the difference between the original data and either of the two aggregation schemes.

Figure 1.2. Effects of aggregation and zoning on spatial patterns. Points in (a) were aggregated using different boundaries (b-d) resulting in a fairly uniform pattern (b) and the appearance of clusters (c,d).

Credit: Blanford, © Penn State University, licensed under CC BY-NC-SA 4.0 [1]

Figure 1.3. Effects of aggregation and zoning on attribute values can affect the variance and/or mean.

Credit: Blanford, © Penn State University, licensed under CC BY-NC-SA 4.0 [1]

Aggregation:

• combines smaller units into bigger units
• affects results!

MAUP is, if anything, more problematic than spatial autocorrelation. It is worth emphasizing just how serious the MAUP effect can be: in a 1979 paper, Openshaw and Taylor demonstrated by simulation that different aggregation (i.e., zoning) schemes could lead to variation in the apparent correlation between two variables from -1 to +1, in other words, the total range of variation possible in the correlation between two variables.

In practice, very little research has been done on how to cope with MAUP, even though the problem is very real. MAUP is familiar to politicians, who often seek to redistrict areas to their spatial advantage in a practice commonly referred to as "gerrymandering." In the practical work associated with this lesson, you will take a closer look at this issue in the context of redistricting in the United States.

Background

In this week's project, we use an example from American electoral politics to revisit the modifiable areal unit problem (introduced in the reading for Lesson 1) and also as a reintroduction to ArcGIS, in case you've gotten rusty. This lesson's project is based on a real dataset. You will begin using the Spatial Analyst extension and learn to convert data between different spatial types. The ease with which you can do this should convince you that many of the distinctions made between different spatial data types are less important than they may at first appear.

Project Resources

1. You will be using ESRI's ArcGIS Pro software (or ArcGIS Desktop 10.X) and the Spatial Analyst and Geostatistical Analyst extensions in this course.
   
   As a registered student in GEOG 586, you can get either:
   • a free Student-licensed edition of ArcGIS Pro.
   • a free Student-licensed edition of ArcGIS Desktop.
     Instructions on how to access and download either of these are available here: Downloading Esri Products from Penn State [4].
2. The data you need for Project 1 are available in Canvas for download. If you have any difficulty downloading, please contact me.
   
   Geog586_Les1_Project.zip -- A file geodatabase containing the data sets required for the project.
   
   Once you have downloaded the file, double-click on the .zip file to launch 7-Zip or another file compression utility. Follow your software's prompts to decompress the file. You should end up with a folder called TexasRedistricting.gdb, which contains a large number of fairly small items.

Questions?

Please use the 'Week 1 lesson discussion' forum to ask for clarification on any of these concepts and ideas. Hopefully, some of your classmates will be able to help with answering your questions, and I will also provide further commentary there, where appropriate.

1. Open ArcGIS Pro and create a new project with the Map template. If you haven't used ArcGIS Pro in another course or at work, or if it has been a while since you've last used it and you need a refresher, you might consider walking through one or two of the QuickStart tutorials [6] on the Esri website to learn about how it is organized a bit differently to ArcMap. One of the nice things about ArcGIS Pro is that it will automatically save your analysis results to the project geodatabase, so you no longer have to set path names.
2. Open the Catalog Pane and use it to add the feature classes in TexasRedistricting.gdb file geodatabase to your project. You will first need to add a connection to the geodatabase so that you can see the files (as you would have in ArcMap). To begin with, you only need to look at districts108_2002. This shows the 32 Congressional districts in Texas in which the Federal elections were held in November 2002. One attribute in this file is RepMaj, an integer value indicating the winning margin for the Republican candidate in each district (a negative value if the Republican candidate lost).
3. Create a map of the RepMaj attribute for districts108_2002. For best results, you should use a diverging color scheme with two color hues, with pale colors showing results near 0, and deeper colors indicating a larger majority for one party or the other (red for Republicans and blue for Democrats is conventional).
4. Make sure to save the project file periodically so that you don't lose work if ArcGIS crashes.

   Deliverable

   Put this map in your write-up, along with a brief commentary (a few sentences or short paragraph will suffice) on what it shows: Are Republican districts more rural or more urban? What other patterns do you observe, if any?

5. Next, look at the tx_voting108 data. This records votes cast for the Republican and Democratic candidates in county-based subdivisions of the districts.

   Note:

   Note that counties and congressional districts are not a precise fit inside one another, so many of the units in tx_voting108data are parts of counties that were subdivided among two or more districts.

6. Also examine the new Districting plan adopted by the Texas Senate in October 2004. This is shown in newDistricts2003. You may find it helpful to put this layer on top and set the fill to 'no color' so that the outlines of these districts are visible over the top of your previous map of the electoral outcome in the 108th Congressional Districts.

   Deliverable

   Comment on the redistricting plan. What would you expect it to do to the balance of the electoral outcome? Can you tell, just by examining this map? Put your comments in your write-up.

In the next few pages, the steps required to estimate possible outcomes of the 2004 election based on the new districting plan by three different methods (Polygon to point, KDE, and uniform distribution) are described, along with an explanation of what each method will do.

After reviewing these methods, you should get involved in the discussions on the course Discussion Forum for this week's project, and then choose one of these methods and proceed to complete the project by producing a map of the estimated 2004 election result. Completion of the project also requires you to comment on your choice of method.

Before using any of the methods, you should check that the Spatial Analyst extension is enabled. You can do this in Project - Licensing. You should see that it says 'Yes' in the Licensed column in the Esri Extensions table.

Once Spatial Analyst is enabled, you should also select the following settings from the Analysis - Environments... menu:

• Under Processing Extent, select 'Same as Layer "tx_voting108"' as the 'Extent'. This ensures that the analysis is carried out to the state boundary.
• Under Raster Analysis, select tx_voting108 as the 'Mask', and for 'Cell size', type a value of 1000 into the box. This will ensure that output raster layers have a cell resolution of 1 kilometer. You may optionally decide that this resolution is rather high and change it to something larger [using too high a resolution can be a problem if you are using the VPN to access ArcGIS Pro, so keep this in mind].

With these settings completed, you are ready to try the alternative methods for generating voter population surfaces.

Option 1: Points at Polygon Centroids

The first option is to make a set of points, one for each polygon in our 2002 voting data, and to use these to represent the distribution of the vote that might be expected in 2004. This approach assumes that it is close enough to assign all the voters in each polygon to a single point in the middle of that polygon.

This is a two-step process, creating a point layer, and converting the point layer to a raster.

To make the point layer:

• Open the Geoprocessing pane from Analysis-Tools. Then search for the Feature to Point tool with the search bar and launch it. Select tx_voting108 as the 'Input Features' and specify a name for the point layer to be produced. You may also optionally choose to force the points to lie inside the polygons.

By whatever means you arrive at a point layer, make sure you understand what is going on here. In particular, check to see if all polygons have an associated center point. Are all the 'centers' inside their associated polygons? (If not, why not?)

Once you have the point layer, you can make raster layers (one for Republican voters, and one for Democratic voters) as follows:

• Search for and launch the Point to Raster tool.
• Select the centroids layer you just made as the Input Features and either REP or DEM as the 'Value field' (you need to make one surface for each attribute).
• Also specify a name for the new raster surface to be created (reps_point or dems_point as appropriate).
• Use 'SUM' as the 'Cell assignment type' (why?).

You will get a raster layer with No Data values in most places, and higher values at each location where there was a point in the centroids layer.

Option 2: Density Estimation from Points

The second option is to use kernel density estimation (which we will look at in more detail in Lesson Three) to create smooth surfaces representing the voter distribution across space. This method requires you to choose a radius that specifies how much smoothing is applied to the density surface that is produced.

The steps required are as follows:

1. Search for and launch the Kernel Density tool
2. Set 'Input data' to the point centers layer (as made in the previous method). For the 'Population field' select REP or DEM (you will be doing both anyway). Set the 'Search radius' to 20,000 [or another value you feel is appropriate for modelling voter distribution], and 'Area units' to 'Square Kilometers'. You should also specify a name to save the output to (I suggest reps_kde, or dems_kde, as appropriate). Then run the tool.

The search radius value here specifies how far to 'spread' the point data to obtain a smoothed surface. The higher the value, the smoother the density surface that is produced.

If you encounter problems, post a message to the boards, and also check that the map projection units you are using are meters (the easiest way to check this is to look at the coordinate positions reported at the bottom of the window as you move the cursor around the map view).

When processing is done, ArcGIS Pro adds a new layer to the map, which is a field of density estimates for voters of the requested type. You should repeat steps 1 and 2 to get a second field layer for the other political party, making sure that you calculate both fields with the same parameters set in the Kernel Density tool.

Option 3: Assumed even population distributions

The third option is to assume that voters are evenly distributed across the areas in which they have been counted. We can build a surface under that assumption and base the final estimated votes in the new districts based on that. This method takes a couple of steps and creates two intermediate raster layers using the Spatial Analyst extension on the way to the final estimate.

A number of steps are required:

1. Search for and launch the Polygon to Raster tool and use it to make new raster layers from the OBJECTID, REP and DEM fields of the tx_voting108 layer. You will need to run the tool three different times, to make three different layers. The default cell size should be 1000 meters. You should specify a sensible (and memorable) name for each raster layer you create (reps, dems, ids are what I used).
2. For the raster layer made from the OBJECTID field, you need to count the size of each area, in raster cells. Search for and launch the Lookup tool from the Spatial Analyst toolbox. Choose your ids raster as the 'input raster', Count as the 'lookup field', and name your output layer ids_count. This will produce a new raster that stores the number of pixels in each voting district. Because you set the cell size to 1000 meters, this is also effectively an area in square kilometers.
3. Now, search for and launch the Raster Calculator tool. Then, for each party, use the Raster Calculator to calculate the number of Republican and Democratic Party voters per raster cell (i.e., per square kilometer) as shown in Figure 1.4. Notice that we used the int function to make sure that we have integers because votes only exist in integer quantities -- there are no partial votes! Make sure you build the expression by clicking on items rather than typing. This will make it less likely that you get a syntax error.
   

   Figure 1.4: Raster Calculator

   Click for a text description of the Raster Calculator Example image.

   The raster calculator expression should be the name of the democrat raster layer divided by the result of the lookup tool. Notice that we used the int function to make sure that we have integers because votes only exist in integer quantities – there are no partial votes! The output parameter should set the name of the output to something informative.

   Credit: Griffin

   ArcGIS Pro will think about things for a while and should eventually produce a new layer (in this case called dems_sqkm). This layer contains in each cell an estimate of the number of voters of the specified party in that cell.

Whichever approach you have chosen to make voter population surfaces, it is the difference between the votes for each party that will determine the estimated election results; so, at this point, it is necessary to combine the two estimated surfaces in a 'map calculation'.

1. Use the Raster Calculator to specify an equation subtracting the estimated Democratic Party voter density from the estimated Republican Party voter density, and run the tool. Call your resulting layer RepMaj plus a suffix that indicates which density surface method you derived the layer from.

You should get an output surface that is positive in some areas (Republican majority) and negative in others (Democratic majority).

Whichever approach you chose to make the Republican majority surface, the final step is to sum the estimated majorities that fall inside each new Congressional District in the newDistricts2003 layer to get a predicted outcome for the 2004 elections:

1. Search for and launch the Zonal Statistics as Table tool. The settings in Figure 1.5 should work (with any necessary changes to layer names):
   

   Figure 1.5: Zonal Statistics as Table

   Click for a text description of the Zonal Statistics as Table Example image.

   The input feature zone tool parameter should be set to newDistricts2003. For the zone field parameter, choose DISTRICT. The input value raster is the layer you created in the previous step. The output parameter should give the result of this tool a sensible name. Finally, the statistics type should be set to SUM.

   Credit: Griffin

   This will make a table of values, one for each new district, which is the SUM of the Republican majority surface values inside that district.

Once you've done this summation, you should be able to join the table produced to the newDistricts2003 layer via the DISTRICT field, so that there is now an estimated Republican majority for each of the new districts. Using this new attribute, you can make a map of the 'predicted' electoral outcome for the new districts similar to the original map for the 2002 election, but based on the estimated Republican majority results.

Here is a summary of the minimal deliverables for Project 1. Note that this summary does not supersede elements requested in the main text of this project (refer back to those for full details). Also, you should include discussions of issues important to the lesson material in your write-up, even if they are not explicitly mentioned here.

• A map of the RepMaj attribute for districts108_2002, along with a brief commentary on what it shows.
• A short commentary on the new Districting plan adopted by the Texas Senate in October 2004.
• A map of the 'predicted' electoral outcome for the new districts in the newDistricts2003 layer similar to the original map for the 2002 election and an accompanying commentary on how you made this map, problems with the method, and any other issues you wish to mention

Please put one of the following into the assignment dropbox for this lesson:

1. A PDF of your write-up, -or-
2. An MS-Word compatible version.

Make sure you have completed each item!

That's it for Project 1!

Throughout this course, a major activity is a personal GIS project that you will develop and research on your own (with some input from everyone else taking the course). To ensure that you make regular progress toward completion of the term project, I will assign project activities for you to complete each week.

The topic of the project is completely up to you, but you will have to get the topic approved by me. Pick a topic of interest, and use the different methods applied during this class to better understand the topic.

This week, the project activity is to become familiar with the weekly term project activities and to think about possible topics and post an idea you have in mind. Each week, the project activity requirements for that week will be spelled out in more detail on a page labeled 'Term Project', located in the regular course menu.

Term Project: Breakdown of Week by Week Activities

The breakdown of activities and points are as follows:

• Week 1: 1 point for posting topic ideas
• Week 2: 2 points for submission of a project proposal
• Week 4: 3 points for on-time submission of a satisfactory revised project proposal
• Week 5: 3 points for feedback to your colleagues on their project proposals
• Week 6: 6 points for the final project proposal
• Week 9: 12 points for quality of the project and the report
• Week 10: 3 points for your involvement in discussions of the final projects

Below is an outline of the weekly project activities for the term-long projects. You should refer back to this page periodically as a handy guide to the project 'milestones'.

Term Project (Week 1) - Identifying a Project Topic

In addition to the weekly project, it is also time to start to think about your term project.

• Review the project outline and become familiar with what is required each week. Timely submission of an appropriate topic suggestion is important at this stage since you will need to provide the entire class a project proposal that will be peer reviewed during Lesson 5.
• This week, you need to provide a minimal description of the project that is identifying a topic and its geographical scope. By minimal, I mean a single paragraph that includes no more than 250 words max!, single spaced, and 11pt or 12pt sized font.
• Timely submission of your preliminary project topic.

Questions?Please use the General Issuesdiscussion forum to ask any questions now or at any point during this project.

Term Project (Week 2) - Writing a Preliminary Project Proposal

Submit a brief project proposal (2 pages max!, 600 words max!, single spaced, and 11pt or 12pt sized font) to the 'Term Project: Preliminary Proposal' discussion forum. This week, you should start to obtain the data you will need for your project. The proposal must identify at least two (preferably more) likely data sources for the project work, since this will be critical to success in the final project. Inspect your data sources carefully. It's important to get started on this early. You do not want to find out in Week 8 that your dataset is not viable! Over the next few weeks, you will be refining your proposal. During Week 5, you will receive feedback from other students. This will help you revise your final proposal which will be due in Week 6.

This week, you must organize your thinking about the term project by developing your topic/scope from last week into a short proposal.

Your proposal should include the following section headers and content for each section:

Background:

• some background on the topic particularly, why it is interesting or a worthwhile research pursuit;

• research question(s). What, specifically, do you hope to find out?

Methodology:

• Data: list and discuss the data required to answer the question(s). Be sure to clearly explain the role each dataset will play.

  • Data Sources: Be sure to list where you will (or have) obtain the required data. This may be public websites or perhaps data that you have access to through work or personal contacts.
    • Obtain and explore the data: attributes, resolutions, scale.
      • Is the data useful or are there limitations?
      • Will you need to clean and organize the data in order to use it?
• Analysis: what you will do with the data, in general terms

  • Analysis Methods: What sort of statistical analysis and spatial analysis do you intend to carry out? I realize, at this point, that you may feel that your knowledge is too limited for this. Review Figure 1.2 and skim through the lessons to identify the methods you will be using. If you don't know the technical names for the types of analysis you would like to do, then at least try to describe the types of things you would like to be able to say after finishing the analysis (e.g., one distribution is more clustered than another). This will give me and other students a firmer basis for making constructive suggestions about the options available to you. Also, look through the course topics for ideas.

Expected Results:

• what sort of maps or outputs you will create

References:

• references to papers you may have cited in the background or methods section. Include URLs to data sources here (if you didn't include the URLs in the Data section.

The proposal does not have to be detailed at this stage. Your proposal should be no longer than 2 pages max!, 600 words max!, single spaced, and 11pt or 12pt sized font. Make sure that your proposal covers all the above points, so that I (Lesson 3 & 4) and others (Lesson 5 – peer review) evaluating the proposal can make constructive suggestions about additions, changes, other sources of data, and so on.

Additional writing and formatting guidelines are provided in the document (TermProjectGuidelines.pdf) in 'Term Project Overview' in Canvas.

Term Project - (Week 3)

Project Proposal: I will be providing each of you with feedback this week on the Preliminary Project Proposals you submitted last week (Week 2).

Peer-review Groups: I will be assigning you groups this week so that you have plenty of time to set up a meeting time during Week 5.

Revising and finalizing your project proposal. Over the next few weeks, you will be refining and extending your term project proposal and receiving feedback from me and your peers. To make this task less daunting and more manageable, we have broken down the process into a series of steps that allows you to evaluate new methods and their applicability to your project as well as receive feedback. Below is a quick overview of the steps each week.

1. Week 3 - You will be assigned a peer review group and receive feedback from the instructor on your preliminary proposal. This week, make arrangements for your group's meeting in Week 5.
2. Week 4 - Post your project proposal to the discussion forum as text and also attach the Word document and/or send it to your group. You should meet with your group during Week 5 for 1 hour at a mutually agreed upon time. Once you have set a date and time, send the instructor the information with the Zoom link.
3. Week 5 - Meet via Zoom for 1 hour. See the instructions below. Make a post to the peer review discussion board about what feedback you found valuable.
4. Week 6 - Further refine your project proposal and submit it to the Final Proposal dropbox.

Term Project (Week 4) - Revising and Submitting a Project Proposal/h3>

Refine your project proposal and post the proposal to the 'Term Project: Peer-Review' discussion forum so that your peer review group can access the proposal.

Your revised proposal should take into account the feedback provided by the instructor in Week 3. Keep the revised proposal to be no more than 2 pages max!, 800 words max!, single spaced, and 11pt or 12pt sized font.

Term Project (Week 5) - Interactive Peer-Review Process

This week, you will be meeting with your group to discuss your proposed project idea.

You should consider the following aspects:

• Are the goals reasonable and achievable? It is a common mistake to aim too high and attempt to do too much. Suggest possible amendments to the proposals' aims that might make them more achievable in the time frame.
• Are the data adequate for the task proposed? Do you foresee problems in obtaining or organizing the data? Suggest how these problems could be avoided.
• Are the proposed analysis methods appropriate? Suggest alternative methods or enhancements to the proposed methods that would also help.
• Provide any additional input that you feel is appropriate. This could include suggestions for additional outputs (e.g., maps) not specifically mentioned by the author, or suggestions as to further data sources, relevant things to read, relevant other examples to look at, and so on.

Remember... you will be receiving reviews of your own proposal from the other students in the group, so you should include the types of useful feedback that you would like to see in those commentaries. Criticism is fine, provided that it includes constructive inputs and suggestions. If something is wrong, how can it be fixed?

Week 5: Term Project - Interactive Peer Review Meeting and Discussion Post Instructions

Now, you will complete peer reviews. You will be reviewing the other group members' proposals for this assignment. Your instructor will divide the class into groups. The peer reviews will take place using Zoom. You should have arranged the time of the meeting with your group in Week 3 or 4.

1. You will arrange to meet with your group for 1 hour using Zoom [7] to interactively discuss your term project. The hour that you meet should be mutually agreed upon by everyone in the group. One team member should agree to be "host."
2. Once you have set a date and time, the session "host" should sign in to psu.zoom.us to schedule the meeting; see instructions in below and circulate the "invitation" details to the other group members.
3. Send the instructor the meeting details (day and time). The instructor will join the meeting if schedules align. If the instructor can not join the meeting, then record the meeting, then send the instructor a link to the recording for a later viewing.
4. Each student has 15 minutes to discuss their project. During this time, each student will describe their project for about 5 minutes and then receive feedback, answer questions, and provide clarifications for the remainder of their 15-minute period.
5. Remember to take notes during your question and answer session so that you can incorporate the feedback you receive into your final proposal.
6. Once the peer review session is finished, to make your post, click Reply in the Interactive Peer Review discussion forum.
7. Click Attach, and select the original term project outline that you uploaded last week (i.e., your revised proposal) and attach it to your discussion post.
8. Post 1 sentence to the discussion for each "peer" in your group that indicates the most useful question, comment, or suggestions you received from each of your Peer Review group members (thus 2 or 3 sentences depending on Peer Review group size) and a second sentence that describes how you are thinking about responding to that feedback.

Zoom: As a PSU student, you should have access to Zoom [7]. Once you have been assigned a group, work with your group to set up a mutually agreed upon date and time to meet via Zoom. One team member should agree to be "host". If you have not used Zoom yet, then use the following instructions to set up a meeting [8].

Term Project (Week 6) - Finalizing Your Project Proposal

Based on the feedback that you received from other students and from the instructor, revise your project proposal and submit a final version this week. Note that you may lose points if your proposal suggests that you haven't been developing your thinking about your project.

In your final proposal, you should respond to as many of the comments made by your reviewers as possible. However, it is OK to stick to your guns! You don't have to adjust every aspect of the proposal to accommodate reviewer concerns, but you should consider every point seriously, not just ignore them.

Your final proposal should be between 600 and 800 words in length (3 pages max!, 1,000 words max!, single spaced, and 11pt or 12pt sized font). The maximum number of words you can use is 800. You will lose points if your word count exceeds 800. Make sure to include the same items as before:

• Topic and scope
• Aims
• Dataset(s)
• Data sources
• Intended analysis and outputs -- This is a little different from before. It should list some specific outputs (ideally several specific items) that can be used to judge how well you have done in attaining your stated aims. Note that failing to produce one of the stated outputs will not result in an automatic loss of points, but you will be expected to comment on why you were unable to achieve everything you set out to do (even if that means simply admitting that some other aspect took longer than anticipated, so you didn't get to it).

Additional writing and formatting guidelines are provided in the document (TermProjectGuidelines.pdf) in 'Term Project Overview' in Canvas.

Term Project (Week 7) - Continue Working on Your Final Project Report

There is no specific deliverable required this week, but you really should be aiming to make some progress on your project this week!

Term Project (Week 8) - Continue Working on Your Final Project Report

There is no specific deliverable required this week, but you really should be aiming to make some progress on your project this week!

Term Project (Week 9) - Submitting Your Final Project Report

Project Overview

Your report should describe your progress on the project with respect to the objectives you set for yourself in the final version of your proposal. The final paper should be no more than 20 pages max! inclusive of all required elements specified in the list below, approximately 10,000 words, single spaced, and 11pt or 12pt sized font. As a reminder, the overall sequence and organization of the report should adhere to the following section headers and their content:

• Paper Title, Name, and Abstract -

  • This information can be placed on a separate page and does not count toward the 20 page maximum.

  • Make sure your title is descriptive of your research, including reference to the general data being used, geographic location, and time interval.

  • Don’t forget to include your name!

  • The abstract should be the revised version of your proposal (and any last-minute additions or corrections based on the results of your analysis). The abstract should be no longer than 300 words.

• Introduction - one or two paragraphs introducing the overall topic and scope, with some discussion of why the issues you are researching are worth exploring.

• Previous Research - provide some context on others who have looked at this same problem and report on their conclusions that helped you intellectually frame your research.

• Methodology

  • Data - describe the data sources, any data preparation/formatting that you performed, and any issues/limitations with the data that you encountered.

  • Methods - discuss in detail the statistical methods you used, and the steps performed to carry out any statistical test. Make sure to specify what data was used for each test.

• Results - individually discuss the results with respect to each research objective. Be sure to reflect back on your intended research objectives, linking the results of your analysis to whether or not those objectives were met. This discussion should include any maps, table, charts, and relevant interpretations of the evidence presented by each.

• Reflection - reflect on how things went. What went well? What didn't work out as you had hoped? How would you do things differently if you were doing it again? What extensions of this work would be useful, time and space permitting?

• References - include a listing of all sources cited in your paper (this page does not count toward the 20 page maximum).

Other Formatting Issues

• Use a serif type face (like Times New Roman), 1.5 line spacing, with 11pt or 12pt sized font.
• Include any graphics and maps that provide supporting evidence that contributes to your discussion. The graphics and maps should be well designed and individually numbered.
• Tables that provide summaries of relevant data should also be included that are individually numbered (e.g., Table 1) and logically arranged.
• For each graphic, map, or image make sure to appropriately reference them from your discussion (e.g., Figure 1) so that the reader can make a connection between your discussion points and each included figure.

Term Project (Week 10) - Submitting Your Final Project Report

Think of this as a virtual version of an in-class presentation of your project with an opportunity for members of the class (and the instructor) to reflect on each other's work.

In order to earn points for this deliverable, you should read through the term papers of those who were in your peer-review zoom session during week 5. Then, post your comments on the papers written by the members of your peer review session in the discussion forum. Here are a few things to consider as you review your group member's write-ups.

• think about the organization of the paper (does the paper flow from an introduction, spatial analysis, and conclusion?)
• what is the research question(s) and it is clearly stated?
• what does the literature review say about the research question?
• what evidence does the analysis provide (is there a spatial component to the evidence)?
• do the results answer the research question(s)?
• is there a conclusion and did you learn anything from the analysis?

These comments can include, but are not limited to, feedback on interpreting the results, make suggestions regarding the methodology, share experiences on the writing process, mention other ideas on the research topic, and so on.

Contributions to discussions of one another's projects will be evaluated, as well as the projects themselves.

In addition to the weekly project, it is also time to start to think about your term project.

• Review the project outline and become familiar with what is required each week (Term Project Overview and Weekly Deliverables [9]). Timely submission of an appropriate topic suggestion (or suggestions) is important at this stage, since you will need to provide the entire class a project proposal that will be peer-reviewed during Lesson 5.
• Post a single paragraph that discusses the following three (3) things about your term project for this class. At this stage of the term project, keep the paragraph to less than 300 words.
  1. What is your term project's main "topic" of focus? Think broadly here (both geographically and conceptually).
  2. Why is your topic important, relevant, timely, etc.?
  3. Describe one (1) data set that you think you will need to collect for your term project. While you are at it, you should verify that you can access the data. Having access to the geographic scale, geographic extent, and temporal dimension of your data is a major hurdle that researchers encounter. Don't put off looking into acquiring your data until later in the course. Later may be too late for the term project.

  Again, we are looking for a "big picture" description of your term project for this deliverable.

Questions?

Please use the Discussion - General Questions and Technical Help discussion forum to ask any questions now or at any point during this project.

Lesson 1 Deliverables

1. Complete the Lesson 1 quiz.
2. Complete the Project 1 activities. This includes inserting maps and graphs into your write-up along with accompanying commentary. Submit your assignment to the 'Assignment: Week 1 Project' dropbox provided in Lesson 1 in Canvas.
3. Post your project topic idea to the project topic discussion forum in Canvas.

Reminder - Complete all of the Lesson 1 tasks!

You have reached the end of Lesson 1! Double-check the to-do list on the Lesson 1 Overview page [10] to make sure you have completed all of the activities listed there before you begin Lesson 2.

Additional Resources

For those of you who work with environmental data, this article might be of interest:

Dark, S. J. & D. Bram. (2007). The modifiable areal unit problem (MAUP) in physical geography. Progress in Physical Geography, 31(5): 471-479.