Massachusetts Institute of Technology
Department of Urban Studies and Planning

11.188: Urban Planning and Social Science Laboratory

Homework 3

Raster Analysis with QGIS

Distributed:

Monday, April 5, 2021

Due:

Wednesday, April 14, 2021 @ 2:30pm


NOTE: This assignment builds directly on the endpoint of Homework 2. To avoid problems resulting from the use of different endpoints from Homework 2 (and to enable everyone to start working on Homework 3 even if they have not finished Homework 2), we provide a shapefile, finalresult.shp, in the HW3 sub-directory of the class data locker. The shapefile finalresult.shp identifies areas that are acceptable locations based on a set of accessibility, health, land use, and demographic criteria similar to (but not the same as) your finding in HW2. Because it was developed using different cutoffs for the criteria, the shapefile differs from the actual answer to your HW2. (do not worry if the shapefile looks different from the shapefile you developed to answer HW2!).

Task 1 [100 points]: Adding 2000 Poor Senior Density as a Criterion and Critiquing the Results

In Homework 2, you identified places that met the accessibility, health, land use, and demographic criteria. You present your first set of results to the non-profit group in charge. Based on the discussion with the non-profit group, you are asked to adjust one of the factors. Instead of just restricting the location to tracts with 'high' counts of poor seniors, you agree that it would be desirable to make sure that the center is close to areas where poor seniors are densest. Consequently, you decide to adjust your analysis by adding another criteria related to the density of poor seniors. Due to data availability issues and the fairly consistent spatial distribution of poor seniors in Cambridge over the last few years, the non-profit group has asked you to use 2000 Census data, at the block group summary level, to calculate the density of poor seniors.The non-profit group provides you with a shapefile of 2000 block groups with the count of poor seniors (field = PoorSen) and the block group area (in meters) of each block group (field = AreaMet). This shape file is called 2000PoorSeniors_BlkGrp.shp and it is located in the HW3 sub-directory of the class data locker.

The HW2 screening exercise identified more than one site that met all the initial criteria. Adding in this new 'density of poor seniors' consideration can help you further narrow down the identification of a preferred site. Among those feasible sites already identified, you would prefer those that fall in the higher density of poor seniors locations. You decide to focus on some measure of 'density' that captures the right sense of 'proximity to seniors' and can be integrated into your site suitability analysis.You know that the census data provides counts of poor seniors (field = PoorSen) for each block group so you can use those data (and the AreaMet field) to compute the density of poor seniors. A thematic map of this measure (by block groups) will show you where the density of poor seniors is high. But that still isn't quite what you want. You would like a measure of how many poor seniors have easy access to the center -- and that measure should include poor seniors from nearby block groups as well as any from the block group in which the center is located. So, you decide to use QGIS's Raster Analysis tools to develop a better measure. First, create a rasterized (grid) version of block groups (with the block group's poor-senior-citizen density as the cell value). Then use the 'neighborhood statistics' tools to create a new grid layer that has, for its cell value, the average of all the cells within some buffer distance (e.g., a circle of 1000 meter radius is what we suggest below).

To implement this plan, create a grid coverage that rasterizes the block groups into grid cells that are 100 meters on edge. Be sure to mask off all but the 5-town area when you create the grid coverage. Set the value in each cell to be your estimate of the density of poor seniors in the block group at the center of the grid cell. Create a thematic map showing the density of poor senior citizens across your Cambridge grid cells.

Hints:

Compute the poor senior density as the count of poor senior citizens per block group divided by the AreaMet field. Note that AreaMet is in square meters -- not in acres. (Recall that 1 hectare = 10,000 square meters = 2.471 acres, and 1 acre = 43,560 square feet.)

Now, use the 'smoothing' (or neighborhood focal mean) tool within the SAGA > Raster Filter > Gaussian Filter part of QGIS's Processing Toolbox to generate a new grid layer where the value of each grid cell is the average (mean) of the density of poor seniors in the surrounding grid cells. Define the neighborhood to be a circle (of grid cells) of 1,000-meter radius (i.e., 10 cells) centered on each grid cell. Notice how the new layer looks different from the original rasterization of your block group densities. (Do you understand why?)

Now, overlay (visually! - no need to 'intersect') this smoothed density of poor seniors layer with finalresult.shp, in the HW3 sub-directory of the class data locker -- the shapefile identifying the locations that we will consider to be acceptable based on criteria similar to (but a little different from) those stated in HW2.

One way to select a specific location for the poor senior center would be to pick the  site (at least 1 hectare in size) among the acceptable locations from Homework 2 that had the highest density of nearby poor seniors. Add another view to the layout of the thematic map you just created which shows this density of nearby poor seniors. Annotate this layout to clarify which map shows the densities: before and after doing the neighborhood averaging.

Summary of Task 1 Requirements:

Part 1A: Turn in one map layout with two views -- one showing the block group density-of-poor seniors before the neighborhood averaging and one showing the density-of-poor seniors after the neighborhood averaging. Adjust and annotate the map so it is readable and add a few sentences explaining what each map is measuring, and comment on any shifts in the patterns that you observe. As in HW2, you will be graded on the quality of both your analysis and your presentation of your results.

Make sure that the neighborhood average view includes some visual representation (i.e. visual overlay) of the finalresults.shp sites so the reader can see which high-density cells fall within the acceptable locations determined in HW2. Doing this is more a matter of attention to cartography and visual display rather than computing a new combined index.

Part 1B: What to Make of All This:  Next, assign a quality-of-location ranking to the top 2-4 acceptable locations in finalresult.shp (using your own judgment) and write a short report (ideally, 1 double-spaced page in length, absolute maximum of 2 double-spaced pages (12-point font, 1-inch margins) interpreting the results of your suitability analyses -- that is, the locations in finalresult.shp and your average density values -- explaining any comments you may have about the allowable locations. Should the density criterion for seniors in poverty be relative or absolute? Would you prefer to relax one of the other criteria and shift the site elsewhere? Would you suggest some trade-offs among the criteria? Do the criteria restrict the sites a lot or a little? Do they appear to capture the individual criteria reasonably?

There are no 'correct' answers to these questions -- and we do not expect you to research the many real-life characteristics of Cambridge that could influence your decision. The intent is for you to back away from QGIS and spend some time reflecting on the problem of locating a poor senior center and thinking about how your specific variables, cutoffs, and visualization tools may have biases, artificial limits, or have otherwise colored or overlooked relevant factors. Why do you think you have (or have not) zeroed in on one or more sensible suggestions for where to put the center?

Recognize that we are not trying to get you to pick a single 'best' location. Rather, we want you to use the data and GIS tools to boil down the many criteria and possible sites to a reasonable and informed discussion of the pluses and minuses of several specific sites. This includes the primary criteria and trade-offs that are likely to be involved in making the selection. You'll want to be able to refer to your Task 1a map in your discussion, and it will be easier to make your points if your maps help the reader visualize the spatial patterns that you've identified for the various criteria (in terms of buffers, land use density, etc.) There will be times in your professional career when you will be asked to conduct an analysis and recommend a single "best" site based entirely on your own analysis. However, there will likely be many more occasions in which you will work with a group of people (experts or general public) to identify criteria, and to weight and rate them. Doing this fairly, using a transparent process, can be difficult. For example, what if two experts or two citizens disagree on what the weights should be? Part of the point in tackling the same problem with different tools is for you to start to gain an appreciation of how the technical tools can influence the decision -- sometimes in subtle ways.

Finally, notice that we've simplified the site selection task to ignore the cost of acquiring the property. Write a few sentences explaining how such cost considerations might steer the site selection toward one or another of the better locations that your analysis identified and what further analysis you might do (using GIS tools and data of the kinds we've been using) to sort through this question. You do not actually have to perform this analysis -- just indicate how you would approach it. You are welcome to reference your answer from HW2, but you should discuss how the additional work in HW3 (this assignment) augmented your analysis.

Deliverables


Homework 2 and Homework 3 developed by Kamal Azar, 1999
Modified 1999-2017 by Joe Ferreira, Anne Kinsella Thompson,Thomas H. Grayson , Myounggu Kang, Jinhua Zhao, Michael Flaxman, Eric Scultheis, Yi Zhu, Juan Camilo Osorio and Hongmou Zhang.
Last modified: April 5, 2021 by Rounaq Basu

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