Map-making and visualization are powerful tools that can facilitate the exploration and understanding of real-world issues across various disciplines, including geography, urban planning, biology, and public health. Eye-catching posters are particularly effective in efficiently conveying messages to large audiences. This project utilizes visualization as a method to explore and analyze complex real-world problems, leveraging the capabilities of mapping to provide clear, impactful insights.
In 2023, the California Department of Water Resources (DWR) continued its extensive monitoring of water quality across the state, focusing on various chemical and physical parameters, including pH levels.Data collected from numerous environmental monitoring stations, as documented in the California Open Data Portal, revealed that the pH levels of both surface and groundwater sources remained within the acceptable range set by regulatory standards. This comprehensive dataset, which includes historical and current water quality data, underscores the effectiveness of California’s water management practices in maintaining safe and reliable water for its residents. The findings from the 2023 monitoring efforts highlight the state’s commitment to ensuring high water quality standards and provide a valuable resource for ongoing environmental and public health assessments.
The primary analysis for the models presented was done using California Water Quality Data from 2023 field site results. Groundwater pH values were focused on to show the relationship between counties with a high population and their groundwater quality. Figure 1 and 2 visually show the pH values per county and their central tendency. There are six data points per county within the statistical analysis and approximately, 7,000 in Map 2. Data manipulation was conducted to specify the top three populated counties in California. The original data source provided various testing variables such as dissolved oxygen and water temperature. The groundwater pH test variable was extracted to perform this analysis.
A healthy pH ranges from 6.5-8.5 (US EPA). In figure 4 it can be seen all three counties are within this healthy range. Significant deviations might indicate pollution or other environmental changes. The box and whisker plot (figure 1) provides a visual summary of the distribution of groundwater pH levels in each county. It displays the median, quartiles, and any outliers in the data, helping to identify the spread and variability of pH levels. The central tendency (figure 2) measures, including mean, median, and mode, provide a summary of the groundwater pH levels in each county. These measures help in understanding the overall pH quality and identifying any significant deviations from the norm. Figure 5 helps in identifying any correlations or patterns in the data. The trend lines intersect with few data points, concluding pH values can vary in range. The kernel density map illustrates the spatial distribution of groundwater pH levels across Los Angeles, Orange County, and San Diego. Areas with higher density indicate regions with more frequent pH measurements, helping to identify hotspots of groundwater pH variations. Kernel density heat maps offer a high level of detail by calculating the density of pH measurements across a region.This helps in identifying small-scale variations and hotspots that might be missed with other mapping techniques. By pinpointing areas with abnormal pH levels, these maps help in prioritizing regions for further investigation and resource allocation. This ensures that efforts are focused on areas that need the most attention In summary, the central tendency and descriptive statistics of groundwater pH values is a key metric for maintaining water quality, ensuring regulatory compliance, protecting the environment, safeguarding infrastructure, and promoting public health. By analyzing the central tendency, water resource managers can make informed decisions about groundwater extraction and treatment, ensuring sustainable use. Future endeavors can include analyzing the other variables provided in the original data sample. In addition, comparing specific variables with one another to find correlation can be beneficial for policy and future change. Lasty, change over time would be a useful analysis to represent areas of heavy groundwater depletion and/or disparity.