LOCAL DATA THAT CAN LEAD TO STORIES

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IRE 2015 CAR CONFERENCE

Lee Zurik – WVUE-TV

lzurik@fox8live.com

@leezurik

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Walker Moskop — St. Louis Post-Dispatch

wmoskop@post-dispatch.com

@walkermoskop

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Gavin Off — Charlotte Observer

goff@charlotteobserver.com

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DOCUMENTS WE REQUEST

SALARY/OVERTIME/REIMBURSEMENTS/EXTRA PAY

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Look at changes year to year

Who makes the most overtime

Total overtime year to year

Other pay – reimbursements, K time, stand-by

DOCUMENTS WE REQUEST

SALARY/OVERTIME/REIMBURSEMENTS/EXTRA PAY

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Is there magic data?

“Body of Evidence” – extra earnings

DOCUMENTS WE REQUEST

CHECK REGISTERS/GENERAL LEDGERS

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Pivot Table/Access – highest paid vendor

Employees large reimbursements?

DOCUMENTS WE REQUEST

TAKE HOME CAR DATA

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Do non emergency workers have take home cars?

Some databases have their driving distance to work

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DOCUMENTS WE REQUEST

CAMPAIGN FINANCE DATA

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Top contributors

Questionable expenses

Check their PAC Accounts too

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DOCUMENTS WE REQUEST

OTHER DATA SETS

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Unpaid Property Taxes

911 Answer/Response time

Lost Property Reports

Good Jobs First – Incentives

Code Violation Data

Supplemental Nutrition Assistance Program (SNAP) spending

Voter rolls/voting records

USAspending.gov

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DOCUMENTS WE REQUEST

WHAT TO DO WITH THE DATA

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Sort it and look at it differently

Compare datasets

Make the data resonate

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Property and housing data

• Property appraisals
• Transactions
• Tax-abated properties
• Locations and owner info/tax info of vacant/condemned buildings
• Code violations
• Low-income housing programs

Property and housing data

Many subsidized housing programs aim to prevent concentrations of low-income housing. Do they succeed?

• Section 8: HUD provides detailed local data
• Check against census data to see if voucher recipients are clustered in poor neighborhoods.
• If so, has that concentration gotten worse over time?
• Check with local housing authority to see if vouchers are being used in homes, smaller complexes v.large developments.
• Which landlords receive the most in subsidies? Are they out-of-state companies?

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Property and housing data

Low-income housing tax credits

• Often combination of state/federal credits administered at state level.
• Certain share of units are required to go to low-income tenants, but often, it ends up being 100% of units.
• Same questions: Are all projects in low-income areas? Are they large complexes as opposed to smaller projects? Are they located near other LIHTC projects/public housing or in areas with high Section 8 use?
• Lots of 911 calls/crime reports?
• Side Q: How much of tax credit actually goes to development?
• Do properties have code violations?
• Are they paying their real estate taxes?

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Property and housing data

Tracking foreclosures, changes in property values, shifts from owning to renting.

Property and housing data

• Properties in tax-increment financing districts.
• Identifying neglectful property owners, code violations.

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Crime statistics

• Look beyond the day’s mayhem. Identify broader trends and add context to daily stories.
• Be aware of data’s limitations.

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Municipal court data

• Warrants issued
• Fines/fees -- do they account for large % of muni revenue?
• Traffic cases
• Combine with census data to find areas that issue disproportionate number of cases/warrants/fines
• Then go to individual munis with high numbers to see case dispositions and breakdowns of the charges associated with warrants. Are they mostly for minor offenses?

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State department of education data

• Test scores: Look deeper than over district scores. State education departments track students at a very granular level.
• How does performance vary by income? By schools within a district? By demographic groups within a school?
• Are there schools who have seen increases in test scores that defy logic?
• Demographic changes.
• Graduation and college readiness.
• Superintendent and administrator salaries.
• Disciplinary records
• Student mobility rates

State department of education data

Taking a closer look at student performance in relation to poverty...

Lottery data

• As state lotteries age, they often struggle to grow. What is your lottery doing to push profits higher? Or is it failing to do so?
• Check administrative expenses
• Prize payouts
• More expensive games
• Reward programs
• Request sales by location to find out where sales are highest. Compare to census data. Are sales growing in poorer neighborhoods?
• Boston Globe found lottery winners who won a statistically impossible number of times.

Handgun or concealed carry permits

• Laws vary by state
• In some states, local sheriffs approve handgun permits
• The state maintains concealed carry data
• Are more people applying for permits?
• The Charlotte Observer matched gun data with felon data and found dozens of felons – some violent - held permits in Mecklenburg County

ATF adverse action

• Federal spreadsheet of federal firearms dealers with denied or revoked licenses
• Includes name of corporation and permit holder, city, data and case status
• ATF also has list on its site of all active dealers
• The Milwaukee Journal Sentinel found 150 gun dealers who were shut down but reopened under different names

Medical examiners

• Tracks suspicious, accidental and unexpected death investigations from beginning to end
• Pinpoint deaths in jails, nursing homes
• The Charlotte Observer found state medical examiners visit the death scene just 10 percent of the time. They fail to view the bodies in one out of every nine cases
• Use in conjunction with state mortality file

State vehicle inspections

• States that require inspections, track garages pass/fail records
• Data include garage location, date, vehicle type, reason for failing
• In Charlotte, garages directly across the street from one another had wildly different records, even thought they served the same cliental
• Do places that sell tires, fail tires often

Purchase cards

• Credit cards used by public workers to by goods without the normal purchasing process
• How much does each department spend
• Is there enough oversight
• Track unusual spending: restaurants, toys, online dating, online gambling
• Be careful. Some purchases may seem inappropriate but are made for legit reasons

State office of the courts

• North Carolina provides a five-year extract – 10 million criminal cases, 60 million civil cases
• Includes defendant, case, offense, judge and trial information
• Great to have on file to background people
• The Observer uncovered a billion-dollar nonprofit hospital system suing poor patients who couldn’t pay their bill

Farm subsidies

• Maintained by the USDA and the Environmental Working Group, a nonprofit
• Data include commodity, conservation and disaster payments
• Include names, business names, addresses
• Look for politicians, attorneys, professors
• Can be matched with campaign finance data, which provides a person’s occupation

Other datasets

• OSHA/MSHA
• State weight and measures
• City tree inventory
• City water use
• Inspections – nursing homes, restaurants, school cafeterias, tattoo parlors, fire hydrants
• Zoo animal deaths
• Pet names…and their owners’ phone numbers