Canadian Water

SONY DSC-RX10M4 9mm f2.4 1/6,400s ISO100

SONY DSC-RX10M4 9mm f16.0 1/100s ISO100

SONY DSC-RX10M4 12mm f16.0 1/40s ISO100

SONY DSC-RX10M4 65mm f16.0 1/640s ISO100

Photos of Sunwapta and Athabasca Falls in Jasper National Park (Alberta), and Overlander, Nairn and Brandywine Falls in British Columbia.

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(Published to the Fediverse as: Canadian Water #photo #waterfall Photos of Sunwapta, Athbasca, Overlander, Nairn and Brandywine Falls in Alberta and British Columbia, Canada. )

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Humpback Whales in the Strait of Georgia

By Robert Ellison.

SONY DSC-RX10M4 220mm f4.0 1/640s ISO100

SONY DSC-RX10M4 178mm f4.0 1/800s ISO100

SONY DSC-RX10M4 220mm f4.0 1/640s ISO100

SONY DSC-RX10M4 220mm f4.0 1/800s ISO100

Photos of three humpback whales in the Strait of Georgia, near Vancouver, British Columbia in Canada.

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(Published to the Fediverse as: Humpback Whales in the Strait of Georgia #photo #whales #humpback Photos of three whales near Vancouver, British Columbia )

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Does closing the Great Highway cause an increase in traffic accidents?

By Robert Ellison. Updated on Tuesday, July 23, 2024.

There is some debate in San Francisco at the moment around permanently closing the Great Highway to traffic.

This road was closed during the pandemic and I regularly visited with my kids to enjoy a bike ride in a safe and beautiful environment. There were also 'slow streets' dotted around the city, many of which persisted. These still have cars and parking and are much less compelling.

Opponents of the plan are concerned that traffic displaced from the Great Highway will make neighborhood streets more dangerous. The hope is that this traffic will shift to Sunset Boulevard but the reality is likely some increased traffic in the Outer Sunset. This seems like a reasonable complaint, but so far I haven't seen any data. Since August 2021 the Great Highway has been closed weekends, holidays and Friday afternoons. Is it possible to see the impact?

I downloaded injury accident and fatality data from Data SF. This covers the entire city so I first cut the data set down to an area bounded by the Great Highway and Sunset Boulevard (including both) and Lincoln Way and Sloat (excluding both). I then picked a pre period from May 28, 2017 to February 29, 2020 and a post period from September 1, 2021 to June 4, 2024. Both periods are 1,008 days which is 36 28 day periods. Here's a chart of injury accidents (number of people injured in total) by week day:

The good news is that injuries are down overall. Monday through Friday the total decrease is 26%. When the Great Highway is closed at the weekend the number of injuries decreases by 44%. It looks like the streets have got safer when the Great Highway is closed. Before the pandemic you would be 14% more likely to get injured at the weekend, for the last few years this has flipped to 14% less likely.

I also looked at deaths. There are only two, both in the post period. One was on a Saturday and one a Monday. That's not enough data to try and draw any conclusions.

Given how nice that stretch of road is, I'm now a big supporter of the Great Highway Park concept.

In case it's useful here's the code behind the analysis:

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Traffic Accidents and Great Highway\n", "\n", "Use the [data set](https://data.sfgov.org/Public-Safety/Traffic-Crashes-Resulting-in-Injury/) from Data SF to understand what impact closing the Great Highway has on traffic accidents and deaths. The data set convers the entire city, so filtering to 37.763814, -122.512532 (top left) to 37.736261, -122.493263 (bottom right) to cover the area with the most impact. \n", "\n", "The great highway was [closed](https://en.wikipedia.org/wiki/Great_Highway) from April 2020 due to the pandemic, and then opened again to cars starting in August 2021. It remaings closed on wekeends, Friday afternoons and holidays. \n", "\n", "Pre period = 2017-05-28 to 2020-02-29\n", "Post period = 2021-09-01 to 2024-06-04\n", "\n", "Both 1,008 days (36 28 day periods, calculated [here](https://docs.google.com/spreadsheets/d/1S92g33eZFrFHkxnITt4n64qkG3uls3RVP2ks3MFaKXk/edit?usp=sharing)).\n", "\n", "Note for deaths, 0 in pre period, 2 in post period, one Sat and one Mon so both open and not open" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\rob\\AppData\\Local\\Temp\\ipykernel_9504\\1758319835.py:1: DtypeWarning:\n", "\n", "Columns (3,15) have mixed types. Specify dtype option on import or set low_memory=False.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "Period=2017-05-28 to 2020-02-29<br>day_of_week=%{x}<br>number_injured=%{y}<extra></extra>", "legendgroup": "2017-05-28 to 2020-02-29", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, "name": "2017-05-28 to 2020-02-29", "offsetgroup": "2017-05-28 to 2020-02-29", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday" ], "xaxis": "x", "y": [ 32, 23, 16, 19, 24, 30, 22 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "Period=2021-09-01 to 2024-06-04<br>day_of_week=%{x}<br>number_injured=%{y}<extra></extra>", "legendgroup": "2021-09-01 to 2024-06-04", "marker": { "color": "#EF553B", "pattern": { "shape": "" } }, "name": "2021-09-01 to 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"paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Great Highway Area Injuries, Pre and Post-Pandemic" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Day of the Week" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Number of Injuries" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv('Traffic_Crashes_Resulting_in_Injury_20240720.csv')\n", "\n", "# the dataset has latitude and longitude in tb_latitude and tb_longitude columns,\n", "# filter to only include records in the bounding box from 37.763814, -122.512532 (top left) to 37.736261, -122.493263 (bottom right)\n", "df = df[(df['tb_latitude'] >= 37.736261) & (df['tb_latitude'] <= 37.763814) & (df['tb_longitude'] >= -122.512532) & (df['tb_longitude'] <= -122.493263)]\n", "\n", "# now filter to only include dates between 2017-05-28 to 2020-02-29 and 2021-09-01 to 2024-06-04 using the collision_date column\n", "df['collision_date'] = pd.to_datetime(df['collision_date'])\n", "df = df[(df['collision_date'] >= '2017-05-28') & (df['collision_date'] <= '2020-02-29') | (df['collision_date'] >= '2021-09-01') & (df['collision_date'] <= '2024-06-04')]\n", "\n", "# Add a column that is set to Pre-Pandemic between 2017-05-28 to 2020-02-29 and Post-Pandemic between 2021-09-01 to 2024-06-04\n", "df['Period'] = '2017-05-28 to 2020-02-29'\n", "df.loc[(df['collision_date'] >= '2021-09-01') & (df['collision_date'] <= '2024-06-04'), 'Period'] = '2021-09-01 to 2024-06-04'\n", "\n", "# group by the pandemic and day_of_week columns and sum the number_injured column divided by 1008\n", "df = df.groupby(['Period', 'day_of_week'])['number_injured'].sum() \n", "\n", "# chart in plotly showing the number of injuries by day of the week for the Pre-Pandemic and Post-Pandemic periods, order day_of_week by Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday\n", "df = df.reindex(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], level=1)\n", "\n", "fig = px.bar(df.reset_index(), x='day_of_week', y='number_injured', color='Period', barmode='group', title='Great Highway Area Injuries, Pre and Post-Pandemic')\n", "\n", "# set x axis title to Day of the Week, y axis title to Number of Injuries, and legend title to Period\n", "fig.update_xaxes(title_text='Day of the Week')\n", "fig.update_yaxes(title_text='Number of Injuries')\n", "\n", "\n", "fig.show()\n", "\n", "# save the chart to a file\n", "fig.write_html('injuries.html')\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 2 }

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(Published to the Fediverse as: Does closing the Great Highway cause an increase in traffic accidents? #politics #sanfrancisco #greathighway Analysis of data from DataSF shows injury accidents are lower when the Great Highway is closed to traffic. )

politics, san francisco, great highway

Time to go, Joe

By Robert Ellison.

I just sent this note to my senators (Padilla and Butler):

"Being a presidential voter in California is like playing the Trolley Problem without a lever. I know my vote won't make much of a difference, but I can no longer vote for President Biden. I was an enthusiastic supporter in 2020. I'm grateful that Biden defeated Trump. I'm proud of the work this administration has done to reduce carbon emissions, make healthcare more affordable and increase domestic semiconductor production.

While impressive, these accomplishments are sunk benefits. If Trump is allowed to prevail in the 2024 election much of this good work will be undone. That will likely be the least of our problems as a country. President Biden is now almost certain to lose the election. We are currently stuck with everyone realizing that this is true, but too few willing to make their position public. We risk a catastrophic election result unless a landslide of hard truths are delivered in the next few days.

Biden ran on a platform of being the bridge to a new generation of leadership. He must fulfil this promise and start the process of passing the torch immediately. As my representative please take a public position that this must happen as I'm sure you must privately believe. Thank you."

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(Published to the Fediverse as: Time to go, Joe #politics #election #biden Open letter to California senators Padila and Butler )

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Canadian Sunsets

By Robert Ellison.

Time lapse of sunsets from Calgary, Banff, Shuswap Lake and Vancouver in Canada (Alberta and British Columbia).

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(Published to the Fediverse as: Canadian Sunsets #timelapse #video #canada #sunset #calgary #banff #shuswap #vancouver A time lapse video of sunsets from Calgary, Banff, Shuswap Lake and Vancouver. )

timelapse, video, canada, sunset, calgary, banff, shuswap, vancouver

Time Lapse of the Milky Way over Jasper

By Robert Ellison.

4K time lapse of the Milky Way over Jasper in Alberta. Shot on July 5 and 6 2024. Sony A7C / 20mm f1.8 G. Post processed in LRTimelapse, Lightroom and Resolve. Music from Stable Audio.

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(Published to the Fediverse as: Time Lapse of the Milky Way over Jasper #timelapse #video #stars #4k #milkyway 4k time lapse from July 5 and 6 2024 shot in Jasper, Alberta, Canada. )

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YVR SFO

By Robert Ellison.

Google Pixel 8 Pro 18mm f2.8 1/653s ISO20

WS1774

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(Recent Photos)

(Published to the Fediverse as: YVR SFO #photo #plane #sfo #yvr YVR SFO )

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SFO YYC

By Robert Ellison.

Google Pixel 8 Pro 7mm f1.7 1/80s ISO21

WS1509

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(Published to the Fediverse as: SFO YYC #photo #plane #sfo #yyc SFO YYC )

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San Francisco Budget - The Missing Manual

By Robert Ellison.

Overview
Organization Hierarchy

Organization Group and Department
Police Department Programs

Object Hierarchy

Character, Object, Sub-object for Revenue
Character, Object, Sub-object for Spending
Sub-objects for the Police Department

Fund Hierarchy

Fund Type and Fund
Fund Type to Organization Group

I recently released my San Francisco Budget GPT. This is based on open data from DataSF, and while it can answer many questions it helps to have enough context to know what to ask. The published dataset has a data dictionary but it can be frustratingly vague. As well as the downloadable data there is a helpful site where you can generate some reports. This post should help fill in some of the gaps.

Overview

Budget data is available from the 2009-2010 to the 2024 to 2025 fiscal years (which run from July 1 to June 30). In the file the fiscal year is at the end of the period. Every dollar has a source and a use, there is a 'Revenue or Spending' column that is Revenue for sources and Spending for uses. The actual dollar values are in a 'Budget' column. There are three hierarchies that allow you to analyze the data - organization, object and fund. Each hierarchy has three levels, and each level has a code and a description. For the rest of this post I'm using data from the 2023-2024 fiscal year.

The total budget for 2023-2024 is $14.6 billion dollars. That's a little hard to put into context. On the one hand it's about two dimes for every week the universe has existed. On the other, it's only a quarter of Elon Musk's Tesla bonus.

Organization Hierarchy

This is the organizational structure of the city - Organization Group, Department and Program. One example is Public Safety, Police, Operating. Here's the full Organization Group and Department structure:

Organization Group and Department

(A quick note on charts - there is a lot of information to present here. Each chart has a full screen button at the top right and I recommend using this to see the most detail. Charts also have tooltips and so you'll have a much better time with this post if you use a big screen and a mouse. On sunburst (nested pie) charts click any segment to filter to that parent only.)

I haven't included Program here. It sounds like it should be useful, the data dictionary says: "For example, the Police Department has programs for Patrol, Investigations, and Administration." But it doesn't:

Police Department Programs

Maybe this worked at some point, but it's not a lot of help for the current year (It's a bit of a mystery, but not like that time I found out someone redistricted the Farallon Islands.)

It's also helpful to know that the department code has been prefixed to every department. So in the data the Police Department is 'POL Police'. This doesn't seem to happen elsewhere in the dataset. It probably helps to find monstrosities like 'MTA Municipal Transprtn Agncy' because we presumably can't find the spare change for longer column names.

Object Hierarchy

My favorite hierarchy is what we're spending money on at a reasonably granular level, broken down into Character, Object and Sub-object. This works both ways and you can look up sources as well as uses. An example on the revenue side is 'Fines, Forfeitures & Penalties', 'Traffic Fines', 'Traffic Fines - Parking' ($98 million).

Character, Object, Sub-object for Revenue

Character, Object, Sub-object for Spending

Sub-objects for the Police Department

While Program doesn't work, Sub-Object allows us to break down Police department spending in more detail:

Fund Hierarchy

Last but not least the fund hierarchy is where the money comes from. The three levels are Fund Type, Fund and Fund Category. As with objects you can also use this on the Spending side to see where funds are being spent.

Fund Type is critical to understanding the budget. There are three main buckets - General Fund, Enterprise Funds and Special Revenue Funds. Enterprise Funds are for self funding departments, so while $1.2 billion of the budget is for San Francisco International Airport that's not coming out of my pocket. The General Fund is discretionary spending and Special Revenue Funds are dollars that have been restricted to specific uses, for example by the endless ballot measures that San Francisco and California are so fond of.

Fund Type and Fund

This treemap shows the first two levels of the fund hierarchy:

Fund Type to Organization Group

Lastly this chart shows the flow of Fund Types to Organization Groups for Spending, excluding Enterprise Funds:

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(All Politics Posts)

(Published to the Fediverse as: San Francisco Budget - The Missing Manual #politics #sanfrancisco #budget Detailed guide to the three hierarchies of the San Francisco Budget from DataSF. )

politics, san francisco, budget

San Francisco Budget Chatbot (GPT)

By Robert Ellison.

I just published a San Francisco Budget GPT to the ChatGPT store. This is free to use, you just need an OpenAI account.

The chatbot is constructed from the 2010-2025 data available on DataSF, the most recent 2025-2026 budget draft and a five year revenue projection. I added some instructions to help interpret the data and I've tested against a range of queries. This is generative AI and so be cautious about what it says. It's pretty good when it can find the right data and will happily invent things if it can't.

Not that this is limited to chatbots. There was a lot of press yesterday around San Francisco being the worst run city in the nation based on this 'analysis' from WalletHub. They are dividing a measure of service quality by budget dollars per capita. This fails to take into account that San Francisco is a city and a county and so the budget includes county services like a Sheriff department. It also ignores that San Francisco runs an international airport and port, not paid for by taxpayers but still in the budget. And it doesn't adjust for regional differences in income that make services more expensive to provide (and higher total dollar taxes to provide them). So I'll take an occasional chatbot glitch over a willfully incurious press pushing out a PR piece that tickles their confirmation biases.

Enjoy, and let me know if it works for you.

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(All Code Posts)

(Published to the Fediverse as: San Francisco Budget Chatbot (GPT) #code #chatgpt #ml #sanfrancisco #gpts #sfpol #budget #openai Custom GPT with 2010-2025 data to answer questions about the city and county of San Francisco. )

code, chatgpt, ml, san francisco, gpts, sfpol, budget, openai

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San Francisco Budget - The Missing Manual

Overview

Organization Hierarchy

Organization Group and Department

Police Department Programs

Object Hierarchy

Character, Object, Sub-object for Revenue

Character, Object, Sub-object for Spending

Sub-objects for the Police Department

Fund Hierarchy

Fund Type and Fund

Fund Type to Organization Group

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San Francisco Budget Chatbot (GPT)

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