Module 6 - Isarithmic Mapping

 

Figure 1 - Isarithmic Map for Washington State Precipitation

In this lab, we explored how long‑term precipitation patterns in Washington are modeled using the PRISM interpolation method. The dataset, originally created by the USDA Service Center Agencies and published through the USDA Natural Resources Conservation Service in 2009, was downloaded from the USDA Geospatial Gateway and includes monthly and annual precipitation rasters. PRISM begins with 30 years of rainfall measurements collected from weather stations, then fills in the gaps between stations using interpolation. Because elevation strongly influences rainfall in Washington, PRISM incorporates a digital elevation model and a regression model that relates each station’s elevation to its precipitation values. These combined factors allow PRISM to estimate rainfall for 800‑meter grid cells, producing monthly surfaces that are summed into an annual precipitation map.

Throughout the lab, we worked extensively with continuous raster data and learned how to apply continuous tone symbology to represent smooth, gradual changes in precipitation. We also used legend‑editing tools to create clear, appropriate map legends and relied on the Spatial Analyst Extension to perform raster operations. Hypsometric tinting was implemented to highlight Washington’s terrain by assigning distinct color bands to elevation ranges, and hillshade relief was added to enhance the visual structure of the landscape. We used the Int tool to convert floating‑point rasters to integers, manually classified elevation data, and generated contours using both the Contour List tool and the Spatial Analyst Toolbar. Altogether, this lab strengthened our ability to symbolize continuous surfaces, interpret terrain, apply analytical tools, and clearly communicate the processes and outcomes involved in building a complete isarithmic map.

Module 5 - Choropleth and Proportional Symbol Mapping

Figure 5 -  Map of European Wine Consumption

 For this project, the Albers projection was used because it preserves area accurately, which is essential for choropleth mapping. Since population density depends on the size of each region, an equal‑area projection prevents misleading distortions. Mapping population density instead of raw population counts also ensures the data is standardized and comparable across countries of different sizes.

A neutral tan color scheme was chosen to represent land, with darker shades showing higher population density. This palette is easy to interpret and avoids overwhelming the reader. Five classes were used to keep the map readable, and the data was classified using Natural Breaks because it best reflected the natural distribution of the dataset. Quantile classification was avoided because it would have grouped very different values together and misrepresented the data.

Wine consumption was displayed using red circles, which stand out clearly against the tan land and blue ocean. The data did not need normalization because population density was already calculated using area. SQL queries were used to filter and manipulate the dataset, allowing for cleaner data presentation and more precise symbol placement.

Graduated symbols were chosen over proportional symbols because they were more user‑friendly and communicated the ranges more clearly in the legend. Although proportional symbols are truest to the raw data, they made it harder to distinguish outliers like Vatican City and were less intuitive for readers. Flannery Compensation was not used; even if proportional symbols had been chosen, it would have exaggerated the Vatican outlier, caused symbol overlap in Europe’s dense geography, and made the legend harder to interpret.

Throughout the project, careful attention was given to cartographic design principles: selecting an appropriate color scheme, choosing a meaningful classification method, creating a clear and accurate legend, using effective thematic symbols, and compiling the final map in a way that communicates the data honestly and clearly. Including the projection information on the map reinforces that the spatial representation is accurate and that the data is being presented faithfully.

Module 4 - Data Classification

In this week's lab, we worked with four common data classification methods, Equal Interval, Quantile, Standard Deviation, and Natural Breaks, to see how each one displays spatial data differently. Using ArcGIS Pro, we created a map layout with four data frames and symbolized each one using graduated colors to make the patterns easier to understand. We also practiced normalizing data, saving custom color schemes, and applying cartographic design principles to produce clear, readable maps. After comparing the classification methods, I evaluated which ones work best for different audiences and which data presentation approach most accurately represents the distribution of senior citizens in Miami‑Dade County.

 Then, two maps were generated using the different methods. he four classification methods each display the senior population data differently and reveal different patterns.

Equal Interval divides the data range into evenly sized classes, which is simple to read but often hides variation, especially after normalization, because most values get compressed into similar categories.

Quantile places the same number of features in each class, preventing empty categories but sometimes splitting similar values or grouping very different ones, which can distort the true differences.

Standard Deviation highlights how far values deviate from the mean, making it excellent for identifying unusually high or low concentrations of seniors, though it becomes less meaningful when the data is not normally distributed or when most values cluster near the average.

Natural Breaks finds the most meaningful clusters in the data, revealing the underlying structure, but the irregular class ranges make comparisons across datasets difficult.

For audiences who need to take action, such as planning senior services, the Standard Deviation method is the most useful because it clearly identifies areas with unusually high concentrations of seniors. For a casual viewer who just wants a general sense of the distribution, Natural Breaks is more intuitive and visually clear.

When presenting this information to Miami‑Dade County Commissioners, the population count normalized by area is the most accurate way to show where seniors actually live. Percentages can be misleading because small populations can appear disproportionately high, and equal percentages can represent very different numbers of people. Normalizing by square miles avoids distortions caused by varying tract sizes and provides a clearer picture of where services are most needed.

Figure 1- Normalized Data

Figure 2 - Data in Percentages