Ben Matheson Data Analyst Anchorage Innovation Team - - PowerPoint PPT Presentation

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Ben Matheson Data Analyst Anchorage Innovation Team - - PowerPoint PPT Presentation

Oct 10, 2023 112 likes •954 views

Ben Matheson Data Analyst Anchorage Innovation Team ben.matheson@anchorageak.gov 343-6980 i-team intro Open data 3 Case studies City Hall Emily Bokar Ben Innovation Matheson Strategist Data Analyst Brendan Babb Patrick

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Ben Matheson

Data Analyst Anchorage Innovation Team ben.matheson@anchorageak.gov 343-6980

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  • i-team intro
  • Open data
  • 3 Case studies
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City Hall

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Brendan Babb Chief Innovation Officer Patrick McDonnell Designer Emily Bokar Innovation Strategist Ben Matheson Data Analyst

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Human centered design Data Technology

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Solve problems in Anchorage

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Improve the lives of residents

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Open data

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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Human centered design Data Technology

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data.muni.org moa-muniorg.hub.arcgis.com

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Case Studies:

  • SNAP Texting
  • Property Tax Exemption Review
  • Building energy prototype
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What’s a good data and automation problem?

Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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data partners timely metrics right-sized problem

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Case Study:

SNAP Texting

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What’s a good data and automation problem?

Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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What’s a good data and automation problem?

Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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Case Study:

Increasing Equity for Property Tax Exemptions

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$46 billion real estate value

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$46 billion $11 billion

Exempted (not taxed)

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$50,000 residential $150,000 senior citizen/ disabled veteran

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48,000 properties with exemptions

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48,000 properties with exemptions

*not all proper exemptions

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  • People move
  • Rent out home
  • Give home to grown kids
  • Life changes
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~100,000

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Automatic flagging of suspicious exemptions and validation of good exemptions.

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If we remove improper exemptions, we can lower taxes for residents.

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We can find senior citizens who should get the exemption, but don’t.

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Use modern data science tools to flag properties for review

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Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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Matheson Benjamin J 06-01-2000 1110 East 20th Avenue, Anchorage, AK 99503 CAMA

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Matheson Benjamin J 06-01-2000 Matheson Ben Joel 06-10-2000 PFD CAMA Matheson Ben 06-10-2000 PFD

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Matheson Benjamin J 06-01-2000 Matheson Ben Joel 06-10-2000 PFD CAMA Matheson Ben 06-10-2000 PFD

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Matheson Benjamin J 06-01-2000 Matheson Ben Joel 06-10-2000 PFD CAMA Matheson Ben 06-10-2000 PFD

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fuzzy matching

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matchFunctionBoth <- function (eachCama, exemptionType, pfdList) { camaDf <- exemptionType %>% filter (`camaParcelId` == eachCama) pfdDfMain <- pfdList %>% filter(pfdDOB == camaBday)

  • utputDfMain <- stringdist_inner_join(camaDf, pfdDfMain, by = c("scName" =

"pfdFullName"), method="lv", max_dist=25, distance_col = "distance") # outputDfMain <-

  • utputDfMain %>% filter(scNameBdayFormat == pfdDOB)
  • utputDfMain <- outputDfMain %>% filter(first5Letters == camaName5)

minDistance = min(outputDfMain$distance)

  • utputDfMain <- outputDfMain %>% filter(distance == minDistance)
  • utputDfMain <- outputDfMain %>% mutate(addressDiff =

stringdist(camaParcelAddress, pfdPHY_ADDR1, method="lv"))

  • utputDfMain <- outputDfMain %>% mutate(addrNumMatch =

ifelse(parcelAddressNumbers == pfdAddressNumbers, TRUE, FALSE))

  • utputDfMain <- outputDfMain %>% mutate(firstLastMatch = ifelse(scLast == pfdLast

& scFirst == pfdFirst, TRUE, FALSE))

  • utputDfMain <- outputDfMain %>% mutate(lastMatch = ifelse(scLast == pfdLast,

TRUE, FALSE)) }

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matchFunctionBoth <- function (eachCama, exemptionType, pfdList) { camaDf <- exemptionType %>% filter (`camaParcelId` == eachCama) pfdDfMain <- pfdList %>% filter(pfdDOB == camaBday)

  • utputDfMain <- stringdist_inner_join(camaDf, pfdDfMain, by = c("scName" =

"pfdFullName"), method="lv", max_dist=25, distance_col = "distance") # outputDfMain <-

  • utputDfMain %>% filter(scNameBdayFormat == pfdDOB)
  • utputDfMain <- outputDfMain %>% filter(first5Letters == camaName5)

minDistance = min(outputDfMain$distance)

  • utputDfMain <- outputDfMain %>% filter(distance == minDistance)
  • utputDfMain <- outputDfMain %>% mutate(addressDiff =

stringdist(camaParcelAddress, pfdPHY_ADDR1, method="lv"))

  • utputDfMain <- outputDfMain %>% mutate(addrNumMatch =

ifelse(parcelAddressNumbers == pfdAddressNumbers, TRUE, FALSE))

  • utputDfMain <- outputDfMain %>% mutate(firstLastMatch = ifelse(scLast == pfdLast

& scFirst == pfdFirst, TRUE, FALSE))

  • utputDfMain <- outputDfMain %>% mutate(lastMatch = ifelse(scLast == pfdLast,

TRUE, FALSE)) }

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  • The vast majority verified

“good”

  • ~4,000 flagged for follow-up

~93% properties matched

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Senior citizens who should get an exemption but don’t

Senior Letter

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Human centered design Data Technology

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Human centered design Data Technology

  • Loss aversion
  • Timeliness
  • Chunking
  • Salience
  • Head start
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Case Study:

Energy Project

(underway)

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10,000,000 square feet of muni properties 150+ M&O maintained buildings $5.7 million - M&O energy annual spend $5 million - Wastewater utility annual electricity

spend

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Our goal: help facility managers use data to save energy through immediate no/low-cost solutions

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Source data: Utility Interval Data 15-minute readings 69,000+ readings between October, 2017 and October, 2019

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  • ff hours

work day

  • ff hours
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  • ff

hours workday

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hours workday

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  • ff

hours workday

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hours workday

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What’s a good data and automation problem?

Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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What’s a good data and automation problem?

Finding a needle in the haystack Early warning tools Prioritizing for impact Automating the mundane

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Stay in Touch: bit.ly/ancinnovation

Ben Matheson ben.matheson@anchorageak.gov 343-6980