Module 5: Choropleth Mapping

 

The goal for this module was to map both Europe population density paired with wine consumption through a thematic choropleth map, allowing for clear comparison between how densely populated a country is with how much wine is consumed per capita. 

To complete this goal, data was evaluated to understand its distribution and exclude any outliers. The choropleth map was then created using quantile classification and graduated color schemes to accurately represent population density. Graduated symbols were then applied to show wine consumption. Lastly, the design was tweaked through adjusting labels and managing overlap. This is where I struggled most in the lab. I initially attempted to use picture symbology, but found that it made the map not easily readable. Additionally, I ran into problems using ArcGIS and had to restart the project. Picture symbology is something I hope to master in the future. 

Overall, This project emphasized the importance of data classification, symbolization and cartographic design. I learned how multiple datasets can be layered together to reveal spatial patterns and comparisons in a clear and engaging way. 

Comments

Post a Comment

Popular posts from this blog

Module 3: Error Handling and Debugging

Image
 Module 3 focused on debugging Python scripts. The lab taught me how to identify syntax, runtime and file path-related errors while improving my understanding of troubleshooting techniques and exception handling. Three scripts were successfully debugged.  Script 1  For the first script, I first received a name error stating that ‘fc’ is not defined. To fix this, I changed it to ‘FC’. The second error I received was another Name error stating that field is not defined. To fix this error, I switched ‘fields’ to ‘field’. The script then ran successfully, as shown above. Script 2  The first error received on the second script was a syntax warning on line 15, saying there was an invalid escape sequence (/p). To fix this, I simply fixed the backslash to the proper form. The second error was a name error on line 14, stating that ‘Filepath’ was not described. To fix this, I just had to change ‘Filepath’ to ‘filepath’ because Python is case-sensitive. The third error encounte...
Read more

Module 1: Crime Analysis

Image
    In this lab, Washington D.C. burglary data were analyzed by selecting burglary incidents, performing a spatial join to count burglaries within census tracts, joining housing data, and calculating burglary rates per 1,000 housing units. The results were displayed as a graduated color choropleth map after excluding outlier tracts.        A kernel density analysis was then completed for assault incidents using a 100-foot cell size and a 1320 foot search radius. The raster was classified into six classes based on multiples of the mean density to identify hotspot areas.       For the Chicago homicide analysis, three hotspot mapping techniques were compared. The grid overlay method counted homicides within grid cells and selected the top 20% of cells. Kernel density was performed using a 100-foot cell size and 2630 foot search radius, with hotspots defined as areas greater than 3 times the mean density. Local ...
Read more

Module 2: LiDAR

Image
   This lab used LiDAR data to create a Digital Elevation Model (DEM), Digital Surface Model (DSM), forest height raster, and canopy density map. LiDAR point clouds were converted into raster datasets, and spatial analysis tools were used to calculate vegetation height and canopy density. The resulting maps and histogram illustrate the distribution of forest structure and demonstrate how LiDAR can be used to assess terrain and support forest management and conservation efforts. 
Read more