Salmon Hatcheries in Washington State

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.
Washington

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.

San Francisco Police Department traffic stop 2017 – visuals

Here are the corresponding visualizations for the python code.

Figure 1: Heatmap to show missing data in the data set.

missing data.png

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.

SFPD demographic chart.png

Figure 3: This is a count plot with seaborn showing the distribution of race description.

histogram of race.png

Figure 4: This is a count plot with seaborn showing the distribution of sex (gender).

sex histogram.png

Figure 5: This is a histogram of age of individuals who had traffic violations in 2017.

age.png

Figure 6: Boxplot with race description and age. The horizontal line in the boxplot indicates the mean.

race and age boxplot.png

 

Figure 7: This is another way of displaying the race description data.

race histo.png

Figure 8: Hexbin plot of race description and age. The cluster of the darker colors show tighter correlation.

hex age.png

Figure 9: This is a lmplot preformed with Seaborn. Comparing sex, race, and time of day.

lmplot age - race - sex.png

 

 

 

 

Trends on emission data since 1990 – 2015

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.

year-trans.png

Figure 2: Residential emissions (in metric tons) between 1990 – 2015.

res v year.png

Figure 3: Commercial emissions (in metric tons) between 1990 – 2015.

com v year.png

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?

E v year.png

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?

EN v year.png

Figure 6: Year Total emissions (in metric tons) between 1990 – 2015. Overall with all the factors has a negative slope.

year v yt.png