Preserving Art for the Future

Have you ever looked at a work of public art and wondered - will that always be here? Will it be around for future generations? The San Francisco Arts Commission asks the same question because their job is to ensure that the city’s Civic Art Collection is preserved for current and future residents and visitors.

But there’s something tricky about public art preservation: every single piece is unique. So how can you estimate the costs to preserve pieces that are by nature non-standard?

Compare public art care to budgeting for roads and bridges. Infrastructure estimates can rely on a vast repository of historical and cross-jurisdictional maintenance costs, as well as careful modeling of material decay. Public art assets have historically been excluded from infrastructure analysis and therefore don’t have this rich discipline and data to draw on.

William McKinley Monument, 1904 by Robert Ingersoll Aitken, located at the end of the Panhandle in Golden Gate Park. Image of fine arts conservator cleaning and applying a protective wax coating to the 5-story tall bronze sculpture. Estimating the costs of restoration is hard given the uniqueness of each piece.

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Background: The Arts Commission must forecast art preservation needs 10-20 years out

Public art enriches public spaces, our cultural landscape, and supports our local art community. As part of the City’s 10-year capital plan, the Arts Commission must generate 10–20-year estimates of the cost to maintain the City’s public art collection. This ensures sufficient funds will be on hand to preserve art as the need arises.

The collection is extensive and growing. As part of every new City construction project, 2% of the budget is allocated to public art. So, the City is always acquiring more public art in need of long-term budget plans and annual preservation priorities.

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Service Question: Estimating art preservation costs is complicated by the very nature of art - uniqueness

Each art piece is unique as a creative piece but also for practical reasons:

• Material used. Maintaining marble, a very robust material, is different than paper or aluminum.
• Location. Some pieces are easy to access, while others are in remote locations - like on a hiking trail.
• Size and scale. Some pieces can fit in a briefcase, while others require scaffolding and ladders to even touch the top.
• San Francisco microclimates. Some pieces are located in the sunny Mission while others face the wind and sea breeze of the Pacific Ocean.

Other factors include historical sensitivity and the starting condition of the piece. For example, a piece in poor condition will cost more to stabilize and move into preservation status.

The lefthand image is Untitled, 2015 by Amy Ellingson, a 10-foot tall, 109 foot long, mosaic mural located at SFO terminal 3. On the right is Jugs on Jugs, 1960 by Robert Arneson, 27-inch-tall glazed ceramic stoneware vase. These two pieces have different restoration needs based on material, location, and size.

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Analytics: Revised cost formula and forecasting improves estimates and helps prioritize pieces

Fortunately, the Arts Commission had already taken significant steps to pin down this tricky estimate. Using their expert knowledge of art preservation, they had identified the core factors listed above, and had already developed the foundation of a formula to estimate the costs.

However, they lacked the expertise to capture the often-tricky interactions among the factors. They needed DataScienceSF’s help to put those factors together in the right sequence, accommodate interaction effects, and add key elements like forecasting, frequency, and other multipliers, such as dynamic condition ratings.

The original formula first took into account size and scale and then used a stability score (a function of material, microclimate, historical sensitivity, and condition) to estimate costs. But the formula was leading to some strange cost estimates. For example, it was estimating the same costs to preserve a piece in good condition with mildly robust materials as a piece in very poor condition.

The Arts Commission's current formula was leading to some strange estimates. For example, it estimated that it would cost the same to conserve a piece made of a robust material and in good condition as a piece made of fragile material and in poor condition.

The revised formula also starts with size and scale, but then uses condition to generate an initial estimate. The estimate is then updated based on material sensitivity and a factor for frequency based on both microclimate and material sensitivity. The long-range projections then incorporate the treatment frequency schedule, interest, fixed costs, and dynamic condition scores.

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Implementation: Easy tool generates updated forecasts with the push of a button

The Arts Commission is already using the new cost formula in the capital planning process and to prioritize preservation work this year.

To make it as easy as possible to use, DataScienceSF built an Excel based tool that allows them to:

• Easily drop in fresh data from their database
• Revise estimates at the push of a button
• Adjust assumptions built into the model

They’ve already made changes to some of the starting assumptions and are hearing significant interest from other jurisdictions hoping to improve their planning process for public art!

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Client Details:

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Client Team

• Jennifer Correia, Project Manager, Civic Art Collection, SF Arts Commission
• Jennifer Crane, Project Manager, Civic Art Collection, SF Arts Commission
• Allison Cummings, Senior Registrar, Civic Art Collection, SF Arts Commission
• Anh Thang Dao-Shah, Senior Racial Equity & Policy Analyst, SF Arts Commission
• Kate Faust, Capital Analyst, SF Arts Commission
• Rebekah Krell, Deputy Director, SF Arts Commission
• Susan Pontious, Program Director, Civic Art Collection & Public Art Program, SF Arts Commission

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Testimonial

The San Francisco Arts Commission is grateful for being selected as a part of the first DataScienceSF cohort. After struggling for a number of years to produce accurate maintenance and conservation cost projections for the entirety of the Civic Art Collection, DataScienceSF brought the exact expertise we needed to break through and complete the project. Our experience working with Joy and her team by far exceeded expectations. They have the unique ability to quickly grasp client goals, are extremely attentive to client needs, and the precision with which they approached our project was unparalleled. We were deeply impressed with how efficiently DataScienceSF worked and the level of communication we maintained with their team. The result is a groundbreaking tool that simplifies our capital budgeting process, helps us determine maintenance priorities, and allows staff to focus energies on actually caring for the collection. Thank you DataScienceSF!!

Allison Cummings, Senior Registrar, Civic Art Collection

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