Should Seattle Convert Public Golf Courses Into Land for Housing?

Like many other metropolitan areas, Seattle is currently dealing with a serious housing shortage. Recently, there have been some good articles (here and here) about converting public golf courses into housing. It is an interesting concept and certainly should be discussed, however one aspect I feel is being overlooked in this debate is how popular are these golf courses anyways? According to Bloomberg News, there is declining interest in golf nationwide, is this happening in Seattle?

The City of Seattle is somewhat aware of this issue and is at least discussing options for the golf courses. I filed a Public Records Request act with the City of Seattle and they sent me data on the rounds of golf played for the past three years.

Unfortunately the city only provided the count data for number of rounds of golf played per year so I cannot look at more granular trends. However, I can plot the counts on an annual basis.

It will be interesting to see if this debate both on converting public golf courses to housing goes anywhere both in Seattle and other major cities. I don’t have a horse in this particular race, I live near the Jefferson Park course and I really appreciate the large expanse of green it provides however I am acutely aware of the need for more housing within Seattle city limits.

Interested in taking a look? I put the full data I got from the city here.

A Visual Ranking of Seattle Public Elementary Schools

One of the things people consistently tell me when they are considering buying or renting a house/apartment in a new location is that they want to move somewhere with “good schools”. This always maks me think what is a “good school” and more importantly, how do we quantify what schools are considered “good”? Generally this means using some sort of list of school data summarized in a fact sheet from a realtor/apartment manager or more likely using a websearch that results in sites such as Niche. This approach is generally fine but it assumes that you are only interested in a specific neighborhood which may be difficult to achieve right now in most major cities across the US and especially in Seattle.

What if instead we looked at school ratings in all neighborhoods simultaneously in a city? This would allow the user to spot trends and make some visual comparisons as well as possibly identify overperforming schools in unexpected areas. Fortunately, Seattle Public Schools (SPS) provides quite a lot of data about their schools which makes this easy to visualize.

Setup

  • For this analysis I just used the SPS data for the 2016-17 school year.

  • I focused solely on elementary school data for the 2016-17 school year. I used the SPS district boundary map for all elementary schools in the City of Seattle and ignored any magnet or alternative elementary schools.

Rankings

My initial questions were focused on what school has the best student/teacher ratio? What school has the best attendance and what schools are good for reading and math?

I took the student/teacher ratio, attendance rate, and reading and math proficiency scores for each school and calculated the rank of that school within the city and made this table. Click on the category name to sort by that category.

School Name Attendance Rank Student/Teacher Ratio Rank Grade 3 Math Rank Grade 3 Reading Rank
Adams 12 53 29 21
Alki 20 35 12 15
Arbor Heights 21 31 45 33
Gatzert 42 4 49 56
Beacon Hill Int’l 1 32.50 30 37
B.F. Day 16 43 25 23
Broadview-Thomson K-8 49 2 46 45
Bryant 6 55 3 4
Cascadia 2 59 1 1
Catharine Blaine K-8 38 15 6 13
Concord Int’l 44 22 59 60
Bagley 10 20 24 19
Dearborn Park Int’l 61 61 61 61
Dunlap 43 5 56 55
Emerson 58 14 43 53
Fairmount Park 22 47 2 3
Coe 9 48 10 9
Gatewood 35 26 33 40
Genesee Hill 24 50 16 20
Graham Hill 39 10 55 49
Green Lake 31 37 26 28
Greenwood 15 54 20 17
Hawthorne 46 16 48 41
Highland Park 48 6 57 50
Hay 8 38 19 10
John Muir 23 25 52 48
John Rogers 33 24 34 34
Kimball 30 32.50 40 36
Lafayette 29 44 27 25
Laurelhurst 34 49 15 24
Lawton 26 42 4 6
Leschi 53 36 37 44
Lowell 60 3 58 54
Loyal Heights 17 58 5 7
Madrona 52 1 47 43
Maple 19 28 31 32
MLK Jr. 45 8 54 58
McDonald International 7 30 23 2
McGilvra 25 40 14 11
Montlake 37 51 9 16
North Beach 14 46 13 12
Northgate 51 11 50 51
Olympic Hills 41 34 21 30
Olympic View 27 56 32 29
Queen Anne 28 57 28 22
Rainier View 55 21 18 26
Roxhill 56 13 53 57
Sacajawea 36 7 42 42
Sand Point 47 27 39 35
Sanislo 57 12 60 59
Stevens 32 29 35 31
Thornton Creek 18 17 41 38
Thurgood Marshall 5 39 8 18
Van Asselt 59 9 51 52
Viewlands 40 19 44 47
View Ridge 3 52 11 5
Wedgwood 11 41 7 14
West Seattle Elem 54 23 38 46
West Woodland 13 45 17 8
Whittier 4 60 22 27
Wing Luke 50 18 36 39
Schmitz Park 62 62 62 62

What jumps out most at me is that no particular school leads all the other schools consistently and it can make it challenging to decide what to prioritize when choosing a school.

Student/Teacher ratio

Each school reports the number of enrolled students and the number of teachers which I simply used to calculate a ratio.

Click on an attendance area for the exact percentage.

Attendance

I was initially interested in student attendance, but the elementary school with the lowest daily attendance was Lowell Elementary with an attendance rate of 89%. Every other school reported an attendance rate at or above 95% which did not make for a very interesting map. I later learned that Washington State has a compulsory attendance law which likely affected these numbers.

Reading proficiency

I was interested in looking at reading achievement scores district-wide for 3rd graders as measured by the Washington State proficiency test

Click on an attendance area for the exact percentage.

Math proficiency

Similarly, I looked at math achievement scores district-wide for 3rd graders as measured by the Washington State proficiency test

Click on an attendance area for the exact percentage.

Family engagement

SPS provides a parent survey with a variety of questions targeted at parent enthusiasm. The results of these surveys are not published but instead we can just look at how many families complete these surveys.

Click on an attendance area for the exact percentage.

The highest school reported that only 49.1% of families responded to the survey which to me means that most of the families are satisified with the school but not too excited or disappointed by their school.

tl;dr Choosing a school is hard but ultimately it comes down to how satisfied the parents or guardians are with the school. Schools report on a wide array of metrics about student performance, but student performance is often an issue of secondary importance when compared to parents’ overall perception of the quality of a school.

Visualizing Flight Data for the 2017 Seattle Mariners

Remember this map that Facebook created of friend connections back in 2011?

I thought it was pretty cool back then and I still think its pretty cool. I wanted to make a similar map but was not sure where to start. I could have done a similar visualization however I recently quit Facebook so I can no longer export all my friend’s data to use for making maps. My next thought was visualizing travel routes such as flight information. I am trying to reduce my carbon footprint which meant I only flew five times in 2017 and have flown exactly zero times so far in 2018. Then I thought, you know who does fly alot? The Seattle Mariners.

First step was to collect all the Mariners game data, fortunately Baseball Reference has all that data in an easily accessible HTML table.

Next step was to geolocate all the stadiums which can be a bit tedious. Fortunately GitHub user the55 created a nice JSON file of all the stadiums and put it as a gist. I was able to use an R library called geosphere for using the Haversine formula to calculate the distance between two stadiums.

My initial attempt here:

In order to make the image look similar to the Facebook connection map, I ended up using this Flowing Data post quite a bit to figure out how to add the lines and change the background color:

Finally because there were so many trips from Seattle to American League West opponents that I ended up adding a bit of noise or jitter to the stadium locations to make the flight paths not perfectly overlap each other.

Looking back at this 2017 reminded me the Mariners finished 78-84 in 2017, here’s hoping to a better season in 2018!

If interested, I put all the code for this analysis here

Further Analysis of the 2017-18 WA State Legislature

This is my second post looking at the data from the 2017-18 Washington State Legislative Session. the first part of this blog can be read here

After some time looking at different bills that did pass, I started to wonder if a bill was more likely to pass if it had more sponsors. First I took the 647 bills passed by the Legislation and signed into law by Governor and looked up how many co-sponsors each bill had:

Then I I took every bill that was introduced but did not become law and counted up the sponsors for these:

So it appears that the number of sponsors is not particulary predictive for a bill becoming law. The three bills introduced in the Senate with the highest number of Sponsors were:

Bill Sponsor count Summary
5598 40 Granting relatives, including but not limited to grandparents, the right to seek visitation with a child through the courts.
6037 28 Concerning the uniform parentage act.
5375 27 Renaming the cancer research endowment authority to the Andy Hill cancer research endowment.

And in the House:

Bill Sponsor count Summary
2282 52 Protecting an open internet in Washington state.
1714 45 Concerning nursing staffing practices at hospitals.
1400 42 Creating Washington state aviation special license plates.

In November 2017, Manka Dhingra won a special election and the Washington State Senate flipped from Republican held to Democrat held. Initially I wanted to focus on the number of bills passed by a Republican held Senate versus a Democrat held Senate but there were too many extraneous variables such as passing a budget and a shorter session in 2018. Instead, I decided to focus on the number of Yea votes by bill

Many of the bills passed were with almost overwhelming support, which is refreshing to see that there is quite a bit of bipartisanship in Washington State in 2018.

As always, analysis code on GitHub

Visualizing the 2017-18 WA State Legislature

In his 2018 State of the State speech, Washington State Governor Jay Inslee made a passioned appeal for a carbon tax and proposed one in Washington State Senate bill 6203. Because of this, I paid more attention to the activities of the Washington State Legislature than I ever had before and I found it fascinating.

First off, lets start with the website for the state Legislature. Here is a screenshot of the Washington State Legislature page for SB 6203 which is the bill I was most interested in:

The website is very resource dense and well worth time exploring when the Legislature is in session. Every piece of proposed legislation shows the same amount of information and allows you to easily find and contact your legislators about a particular bill if interested. The site also has livestreams of committee hearings and displays vote counts on bills in almost real time as the votes are tallied on both the Senate and the House floor.

Is Washington State unique in this regard? Of course not, here is a screen shot for an interesting bill in Legislature for the State of California.

Finally here is a screenshot of a House bill on the United States Congress website

Does ease of use of the website increase participation in the civic process at the state level? That is a difficult question to answer but personally I am glad I get to use the Washington State one instead of the California State Legislature webpage.

The 2017-18 Washington State Legislative Session ended on March 8, 2018 and Governor Inslee then had 21 days to sign bills into law or veto them.

The conclusion of the 2017-18 Session made me wonder what happened to those bills that were introduced and how many of them actually became law. In addition to a great website, the Washington State Legislature also has an excellent set of Web Services that allow for programmatically capturing metrics and data about activities in the state legislation. One way to easily visualize this is with a Sankey Diagram (no relation to this Sankey though).

Here is a smaller image of the diagram with a larger version here

Code to generate this figure available on my GitHub repo

Has the Pac-12 Network Decreased UW Home Football Game Attendance UPDATED

Following up on my earlier post, how much has the Pac-12 Network affected game attendance? I updated my previous data set to include the past two seasons so as to include 2008-2017 data. I relied on home game attendance as reported by Wikipedia and also used Wikipedia to determine what TV network broadcast each home game. In an ideal world I would be able to make better comparisons using the Nielsen rating for each game however my guess is that data does not come as cheap or as easily as data from Wikipedia. For the purposes of this analysis I am neglecting various other factors in this anaysis such as time at kickoff, game day temperature, opponent, ranking of UW, ranking of opponent, etc… the list goes on and on. My main intention was to simply show home game attendance versus TV network for all games:

And attendance for Pac-12 only opponents versus TV network:

Based on the available data it appears that attendance during home games has been influenced and possibly decreased by the Pac-12 Network but it is difficult to say for sure while ignoring so many external factors. With a significant budget deficit still a major issue, one can only hope that losses from game day ticket sales are made up for with Pac-12 Network advertising revenue.

States With Multiple Football Teams in the AP Top 25

With WSU beating Oregon and UW beating UC Berkeley, the State of Washington is poised to have two football teams in the top ten of NCAA Division I football rankings. Naturally this got me thinking, how often does this happen and how many states have had this same achievement?

To answer this I used the weekly results of Associated Press poll which started in 1936 and thanks to our good friends at Wikipedia, I was able to get AP Poll results for every week.

I found that 25 states had at least one week where two teams from that state were in the AP Poll. However, the more I thought about it the more I realized this was slightly biased because some states might only have one team (i.e. Wyoming) while other states might have two Division I teams that are never both great at the same time (i.e. Montana). I tightened down my restrictions a bit and only looked at the top 10 teams from each AP Poll.

Surprisingly, of the 25 states with at least two teams in the AP top 25 Poll, 21 of those states had a week with at least two teams from that state in the AP top 10. I made a summary table with the most recent year each state achieved this distinction listed:

state year
Louisiana 1936
Maryland 1955
North Carolina 1957
New York 1958
Illinois 1963
Indiana 1979
Pennsylvania 1982
Colorado 1994
Kansas 1995
Washington 1997
Ohio 2009
Oregon 2012
Florida 2013
South Carolina 2013
Georgia 2014
Mississippi 2014
California 2015
Alabama 2016
Michigan 2016
Texas 2016
Oklahoma 2017

Then, I thought what if there were ever a week when a state had 3 teams in the AP top 10. Sure enough, four states have achieved this:

state year
California 1952
Indiana 1967
Florida 2005
Texas 2015

As always, all of my code for this is on GitHub

Further Exploration of IMDb TV Show Rating Data

I wanted to revist my previous post continuing to look at using linear regression for determining the best episodes of a TV show to watch. I started to think about how to look at this data for multiple TV shows. Performing a linear regression on show rating by episode number within a season quickly allows us to determine the maximum and minimum residual for all the show episodes. I took this a step further and calculated which episode of the show it was. For example, here are all the episodes with residual value for that particular show Master of None:

Season Episode Name Residual count appearance
1 1 Plan B -0.28 1 0.05
1 2 Parents 0.21 2 0.1
1 3 Hot Ticket 0.01 3 0.15
1 4 Indians on TV 0.21 4 0.2
1 5 The Other Man -0.09 5 0.25
1 6 Nashville 0.31 6 0.3
1 7 Ladies and Gentlemen -0.39 7 0.35
1 8 Old People -0.09 8 0.4
1 9 Mornings 0.11 9 0.45
1 10 Finale 0.01 10 0.5
2 1 The Thief 0.44 11 0.55
2 2 Le Nozze -0.36 12 0.6
2 3 Religion -0.27 13 0.65
2 4 First Date 0.13 14 0.7
2 5 The Dinner Party 0.02 15 0.75
2 6 New York, I Love You 0.42 16 0.8
2 7 Door #3 -0.89 17 0.85
2 8 Thanksgiving 0.31 18 0.9
2 9 Amarsi Un Po 0.30 19 0.95
2 10 Buona Notte -0.10 20 1

We can see that the episode with the highest residual is S2E1 “The Thief” and the episode with the lowest residual is S2E7 “Door #3”. For every TV show I took all the episodes and calculated their order as a percent of the total number of episodes - for example the pilot episode would be 0.0 and the series finale would be 1.0 to generate an index. I then took the maximum and minimum residual values for each show and plotted them against that episode. For example here is a plot of just Master of None:

To obtain data on as many shows as I could I used this IMDb list of shows with over 5000 votes and selected the first 1200 shows as a dataset. I then reused the OMDb API as I did before. I then calculated the same values as I did for Master of None above and plotted them in a similar manner (use the mouseover for more information on each point):

Two things immediately jump out at me:

  1. The density of points right around the zero line shows that linear regression is a pretty good metric to use for this type of analysis and that most people rate the show generally in line with the overall trend for that particular season.

  2. There seems to be a tendancy for people to really love or really hate the series finale of TV shows and this shows up by the sheer number of points at 1. Possibly this is people expressing their overall view of the show as a whole or maybe people really were really happy or unhappy with the series finale.

I put some of the main code I used in a GitHub repository

Smarter Binge Watching With Linear Regression

I am not much of a binge watcher but I do enjoy quality TV shows which is why I think GraphTV is so great. GraphTV plots the IMDb user ratings for every episode and then performs a linear regression of the episode rating by the episode number to create a trend line which helps you see if the show gets better or worse over the course of the season.

This is nice but it can get difficult to use GraphTV for shows like Golden Girls and downright impossible for shows like The Simpsons.

To solve this I created a GitHub repo binge-trendy. Because the trend line is fit to the IMDb user rating data, we are interested in which episodes do IMDb users think are better than the regression model predicts which translates to any deviation from the trend line. Since I am only interested in episodes that are rated higher than the regression model would have predicted, I only look at episodes with a positive residual.

For example, Golden Girls season 4

Season Episode Name
4 1 Yes, We Have No Havanas
4 2 The Days and Nights of Sophia Petrillo
4 6 Sophia’s Wedding: Part 1
4 9 Scared Straight
4 11 The Auction
4 14 Love Me Tender
4 15 Valentine’s Day
4 19 Till Death Do We Volley
4 20 High Anxiety
4 22 Sophia’s Choice
4 23 Rites of Spring
4 24 Foreign Exchange

I realize the code is not great, pylint currently gives it a 6.05 but if there is one thing I have learned in software:

Standing Up for Net Neutrality

Currently there are many political issues that demand attention however in my opinion there are none that would affect more people than the possible destruction of net neutrality.

Net neutrality is simply the principle that all data on the internet should be treated the same. It does not matter if you are visiting Fox News or Mother Jones - the data and content from both of these websites (as well as from every other website) should be treated as equal and that data should be served equally by all Internet Service Providers. Losing net neutrality could lead to an internet that favors one of these two sites based on which site is willing to pay more. I chose these sites because they are such polar opposites but at the same time we live in a country that allows for such opposites to have equal protection of freedom of speech. I may disagree with the content of a particular website but I do not think it should be served any differently than the website of a site I do agree with. Destruction of net neutrality will lead to greater influence wielded by larger corporations and could stifle smaller websites and startups.

Fortunately, there is still time to act. On July 12, various online communities and users will come together to stand tall and sound the alarm about the FCC’s attack on net neutrality. Join us here!