by Dane Miller
The State of Washington contains 1166 dams within the state. The Columbia River contains more than 60 dams, containing some of the largest dams in the United States. However, the fish ladders the allow salmon and steal head to move upstream stop at Chief Joseph Dam in Bridgeport, Washington (47.9953° N, 119.6333° W). There is roughly a 500 mile stretch of the Columbia River where salmon and steelhead trout from the Pacific Ocean can not migrate upstream to the headwaters due to the Chief Joseph Dam.
Here is a map of the Columbia River watershed.
Columbia River Watershed
In this post I have mapped the salmon hatcheries in Washington State using folium in Python.
Here is the salmon hatchery interactive map.
Here is a map of the dams in Washington state.
Here is the interactive map by The Northwest Power and Conservation Council.
Dams along the Columbia River
Perhaps one day the Chief Joseph Dam along the Columbia River will be removed and salmon could one day return to the upper section of the Columbia River.
Here are the corresponding visualizations for the python code.
Figure 1: Heatmap to show missing data in the data set.
Figure 2: San Francisco Police Department demographics of race description by sex (gender). Where M indicates Male, F indicates Female, and U is Unidentified. The vast majority of the traffic incidents in 2017 were by white males. Nearly 2 times as much as the next race description category.
Figure 3: This is a count plot with seaborn showing the distribution of race description.
Figure 4: This is a count plot with seaborn showing the distribution of sex (gender).
Figure 5: This is a histogram of age of individuals who had traffic violations in 2017.
Figure 6: Boxplot with race description and age. The horizontal line in the boxplot indicates the mean.
Figure 7: This is another way of displaying the race description data.
Figure 8: Hexbin plot of race description and age. The cluster of the darker colors show tighter correlation.
Figure 9: This is a lmplot preformed with Seaborn. Comparing sex, race, and time of day.
Trends on emission data since 1990 – 2015
Source for the data: https://catalog.data.gov/dataset/greenhouse-gas-emissions-from-fuel-combustion-million-metric-tons-beginning-1990
The following plots are conducted in Seaborn Jointplot Python code: jointplot sns.jointplot(x=’Year’,y=’Residential’,data=df,kind=’reg’)
Figure 1: Transportation emissions (in metric tons) between 1990 – 2015.
Figure 2: Residential emissions (in metric tons) between 1990 – 2015.
Figure 3: Commercial emissions (in metric tons) between 1990 – 2015.
Figure 4: Electricity Generated emissions (in metric tons) between 1990 – 2015. This is a pretty strong trend of electricity generated has decreased considerably. Question might be how is the US generating enough electricity to be sustainable?
Figure 5: Net Electricity emissions (in metric tons) between 1990 – 2015. Curious where is the net electricity being stored? Is this coming from dams, solar, or wind?
Figure 6: Year Total emissions (in metric tons) between 1990 – 2015. Overall with all the factors has a negative slope.