PHLT 8076 Week 11 Final Map Synthesis Paper Example

One Map, One Argument: Where Breast Cancers Are Found Late in a Composite County, Defended Choice by Choice Against the ZIP Code Dashboard It Replaces

Student Name

Walden University

PHLT 8076: Fundamentals of Mapping and Geographic Information Systems for Public Health

Instructor Name

Month Day, Year

One Map, One Argument: Where Breast Cancers Are Found Late in a Composite County, Defended Choice by Choice Against the ZIP Code Dashboard It Replaces

The Map and Its Argument

The course's final map shades all 152 tracts by the share of breast cancers in women aged 40 and older, diagnosed from 2018 to 2022, that were already late stage, using five natural-breaks classes of a sequential palette, with hatching on the eleven tracts that had fewer than ten diagnoses. Mammography facilities, including those across the county line within a 30-minute drive, appear as points, placed according to the years they were open. Its argument is this: late-stage diagnosis in the county concentrates in a group of southern tracts, and that concentration aligns more closely with tract poverty than with distance to screening. All values are composite.

The map replaces a dashboard that shaded ZIP codes by counts of late-stage cases. This paper defends each of the new map's choices against the dashboard's, drawing on the work of earlier weeks, and then names what the map leaves unproven.

Choice 1: Rates, Not Counts

The dashboard mapped counts, which follow population and made the urban east side look like the problem. The final map uses the percentage of diagnoses found late, which compares tracts on equal terms and speaks directly to whether screening finds cancers early. Late-stage incidence per 100,000 women, age adjusted, appears as a companion map, because tracts that score poorly on both measures make the clearest case for attention. Monmonier (2018) shows how count maps are routinely read as risk maps, and the change from counts to percentages is the single largest correction the final map makes.

Choice 2: Tracts, Not ZIP Codes

ZIP codes in the county range from compact urban areas to a rural route district spanning forty minutes of driving. Tracts separate the town around the southern hospital from the farmland beyond it, are designed to describe populations and supply census denominators. Using the registry's geocoded points rather than its tract codes placed every diagnosis consistently in 2020 tracts despite boundary changes. The modifiable areal unit problem means any unit shapes the pattern (Openshaw, 1984), but tracts at least draw lines where access differs.

Choice 3: Natural Breaks, With Quantiles Shown

The tract percentages are skewed, with a distinct high group. Natural breaks isolates that group; quantiles, which perform well for map series (Brewer & Pickle, 2002), split it across classes. Because this is a single map with a specific argument, natural breaks is used, the legend names the method and the quantile version appears in an appendix so readers can see how much the southern cluster's prominence depends on classification. The small tract at 80% is hatched so it cannot set the scale, the fault that hid the pattern on the dashboard.

Choice 4: An Extent That Follows Women, Not Jurisdiction

The map shows the county at county scale, with a state locator inset, and extends to facilities within a 30-minute drive beyond the county line. That extension came from a classmate's map in Week 6, which, cropped at the line, made the southern tracts look forty minutes from screening when a regional center sat four miles across the border. With that center included, most southern women live within fifteen minutes of a facility.

Choice 5: Layers Matched in Time

The provenance audit found that the current facility list would misstate access during the study period, since two southern facilities closed in 2019 and 2021 and one opened in 2023. The final map uses facility locations rebuilt for each year, so drive times reflect the facilities open when women were diagnosed. Social vulnerability scores, built for emergency planning rather than cancer screening, appear as context in a companion panel, with that purpose stated (Flanagan et al., 2011).

What the Map Shows

The southern cluster of high late-stage percentages remains under every version of the map. Once cross-border and year-specific facilities are counted, drive times in the cluster are not longer than in several northern tracts with low percentages. The cluster does coincide with the county's highest social vulnerability scores. That pattern is consistent with a large registry study in which poverty and insurance, not travel time, were associated with late-stage diagnosis (Henry et al., 2011).

What the Map Leaves Unproven

The map does not show that poverty causes late diagnosis; it shows that the two coincide across tracts. It cannot say that the women diagnosed late were the poorer women in their tracts, the ecological fallacy that Robinson (1950) described. It rests on registry geocoding that placed 6% of addresses less precisely, concentrated in the rural south. And it describes five years, too few diagnoses in some tracts for stable percentages, which the hatching acknowledges. Each limit is stated in the map's caption.

What the Program Should Do With It

The map points the screening program toward the southern tracts, but its alignment with social vulnerability rather than distance suggests that outreach there should address cost, insurance enrollment and follow-up after abnormal results, not only transportation. The program plans to pair the map with interviews of women diagnosed late in those tracts, which could test what the map can only suggest. The map will be updated when the next five years of registry data are available, using fixed class breaks so the two periods can be compared directly. Those fixed breaks will be the ones chosen here, so the first map in the series doubles as its baseline. If the southern cluster persists in the next period after outreach targeted at cost and insurance, that finding would itself be informative and would prompt a closer look at the barriers women there describe.

Why the Comparison With the Dashboard Matters

Readers who saw the dashboard drew a conclusion about the east side that the data did not support. The final map's value lies partly in correcting that impression, so the paper places the two side by side in its appendix with a sentence explaining each difference. A map that replaces another should show readers what changed and why.

References

Brewer, C. A., & Pickle, L. (2002). Evaluation of methods for classifying epidemiological data on choropleth maps in series. Annals of the Association of American Geographers, 92(4), 662-681. https://doi.org/10.1111/1467-8306.00310

Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8(1), Article 3. https://doi.org/10.2202/1547-7355.1792

Henry, K. A., Boscoe, F. P., Johnson, C. J., Goldberg, D. W., Sherman, R., & Cockburn, M. (2011). Breast cancer stage at diagnosis: Is travel time important? Journal of Community Health, 36(6), 933-942. https://doi.org/10.1007/s10900-011-9392-4

Monmonier, M. (2018). How to lie with maps (3rd ed.). University of Chicago Press.

Openshaw, S. (1984). The modifiable areal unit problem. Geo Books.

Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351-357. https://doi.org/10.2307/2087176