Welcome To Chattalytics!

The City of Chattanooga's Data Science Program.

Solving real world problems with data.

What is Chattalytics?

Chattalytics is a program to proactively use data instead of reactively. Using the methods and tools of data science, the city will identify projects that help solve real world problems with data.

Analytics is an approach that:

uses data to generate new insights into city services and the needs they serve
applies these insights to improve service delivery
helps departments work smarter – using existing data sources and in-house technology to achieve better results with existing resources

Keep scrolling down or use the following links to learn more about Chattalytics.

Learn about the different type of analytics projects.

See if you have a good candidate Chattalytics project.

Check out our introductory presentation.

The different types of analytics projects?

Analytics can help you solve many different types of problems you commonly face in your department such as:

Finding a needle in a haystack - Predictive modeling can be used to pick out hard to find targets based on existing data sources.

Prioritizing work for impact - New ways of using information to prioritize high-impact or easy-to-resolve cases.

Early warning tools - Predicting need from historic patterns to inform predictive deployment or new services.

Better, quicker decisions - Recommendation tools can bring better, actionable data to operational decisions.

Optimizing resource allocation - Improved efficiency from data-driven deployment of teams and assets.

Experimenting for what works - Experimental testing to refine and improve services.

Details of the different analytics projects.

Needle In a Haystack

Sometimes finding out who or what to target for your service or policy is the hardest part. By using data science you can quickly find the target candidates.

Below is an example of how Chattalytics can help you find your needle in a haystack.

Example Problem	Potential solution	Example
Searching for regulatory non-compliers. Regulatory teams are tasked with identifying a small number of problem businesses. They must sift through thousands of applications, often with only the option of a random audit, which is time-consuming, places burdens on compliant businesses and has a low conversion rate.	Predictive modeling uses data on old infractions to identify those at highest risk. The characteristics of filings from businesses who have broken rules in the past can be used to predict the types of business most likely to be non-compliant in the future. This can be used to create tools to select cases for audit; audits can be randomized over this higher-risk group, with selection criteria refined over time.	In assessing compliance with restaurant waste disposal regulations, NYC cross-referenced industry data on grease production with restaurant permit data and sewer back-up data from city agencies, allowing them to better-predict waste violations and to target enforcement (read more here).

Can the needle in a haystack approach help your department?
If your service targets a small number of individuals (either those at high-risk, or most likely to be non-compliers) with regulation—and locating them is challenging.

For example:

Target clients are known to be distributed across the city and not easily located with current outreach programs.
In a sea of regulatory data, non-compliers are difficult to identify without a costly audit.
Priority service users are likely to use other government services and could potentially be identified in this way.

Prioritizing Work for Impact

Example problem	Potential solution	Example
Backlog of pest control cases are processed without information on their potential severity. Ordinarily, cases are investigated and dealt with in the order that they are received. Although pest control teams have insights as to which cases are most likely to lead to serious outbreaks, calls are not classified until an initial inspection.	Predicting the danger of outbreak posed by new cases before inspection, and reaching those cases first. Using data about the location, timing and nature of the complaint and comparing this to past outbreaks, cases can be ranked according to the risk that they turn into full outbreaks. These cases can be prioritized with quicker inspection and/or initial deployment.	In Chicago, after reviewing data on historic outbreaks, incidences of 311 requests related to garbage or broken water mains close to pest reports were used to identify severe outbreaks and trigger an immediate response (read more here).

Can this approach help your department?

Work is currently assigned un-strategically: on the basis of first-come-first-served, randomly or constituent complaints.
Services allocate and engage with clients before important information on the complexity and need of a given case is known.
Services could be more efficient if high- and low-complexity cases could be sorted earlier, or if higher-need cases could be identified more quickly.
Service backlog contains cases that vary by complexity and need, but:
- Backlogs are dealt with in random or first-come-first-served order; or
- Information to sort these cases in a productive way is not available until a case starts.

Early Warning Tools

Example problem	Potential solution	Example
Excessive force violations by police officers have huge negative repercussions in the community and for police careers. Targeted early interventions could enable forces to prevent such interactions, rather than responsively dealing with the officers after an incident occurs.	Refine early warning system, identifying officers most likely to have negative interactions with the public. These systems flag recurring complaints against officers and notify supervisors when certain thresholds were reached, such as a certain number of use-of-force complaints over a given period of time, so those supervisors could implement targeted interventions.	In Charlotte, teams worked to enhance existing early warning tools by making better use of existing data. Combining information on officer demographics, training, payroll, on-the-job actions, internal affairs data, dispatch data, negative interaction reports and publically-available data, they were able to increase the accuracy of the CMPD system by 15-20% while reducing false positives by 55% (read more here.

Signs that this type of project could help your department

Resources are deployed in response to need, rather than pre-empting it.
Services struggle to predict or respond to spikes in demand.
Department wishes to enhance preventative options, but investment is held back by inability to predict the occurrence of need.
Intelligence from the public on potential service need is available, but has not been brought into service decision-making in a structured way.

Better, Quicker Decisions

Example problem	Potential solution	Example
EMS dispatchers are required to make dozens of daily deployment decisions, often with incomplete information. Decisions can be delayed if crew readiness information is not available, or skewed if accurate traffic data not available.	Provide teams with traffic and hospital-turnaround adjusted estimates of crew readiness. Using accumulated data on dispatches, delays in response times caused by dispatch decisions could be assessed. Identifying hospitals and traffic corridors that can cause delays in response time could allow teams to better identify optimal teams for deployment.	In Louisville, dispatchers are supported with regular reports from computer-aided dispatch software which detailed patterns in turnaround times for specific hospitals and for specific crews (read more here).

Can this approach help your department?

Services involve repeated operational decisions - such as those deployment, resourcing or enforcement – which create friction for the department.
Decisions taken by teams require significant judgement, due to lack of information, which could be supported with more structured information
Service teams are delayed in deployment or mis-deployed because decision-makers do not have important information in a usable format

Optimizing Resource Allocation

Example problem	Potential solution	Example
Ambulances’ standby locations are chosen based on dispatchers’ habits or their instincts as to where they could most easily get to emergencies. Based on their personal experience, dispatchers and teams work with only a limited view of total EMS demand in the city.	Standby deployment based on city-wide analysis of accident patterns, traffic patterns and crew readiness. Building on the insights of dispatchers and ambulance crew with city-wide data on accidents, hospital transfers and traffic, ambulances can be deployed in a way that improves coverage and reduces emergency response time.	In New Orleans, a project is underway to map optimal standby locations for ambulance crews, given the patterns of emergency calls seen in the city.

Can this approach help your department

Department resources have been scheduled or deployed in the same way for a long time, despite changing patterns of need.
Delays in service response times could be reduced with smarter deployment of assets closer to anticipated need, or with scheduling which better matches spikes in demand.
With better information, teams could be deployed more flexibly geographically or across shifts.

Experimenting for What Works

Example problem	Potential solution	Example
Outreach tools, such as SMS texts, to Medicaid clients deliver low conversion rates. Those at risk of missing important medical appointments are texted reminders, but impact seems to be limited.	Optimize SMS outreach services with local testing of different forms. With relatively little disruption, different messages and timing of texts could be tested to find combinations that delivered maximum impact for different groups.	In New Orleans, experimental A/B testing was used to refine the content of text message outreach to Medicaid waiver program recipients, to maximize conversion rates (read more here).

Can this approach help your department?

Low-cost engagement tools such as letters, texts and reminder calls are not used, or demonstrate low conversion rates that suggest a possibility for improvement.
Departments want to refine service tools, but local impact of new or established tools is hard to gauge before a full roll-out.

Do I have a good Chattalytics project?

In looking at other successful analytics programs in other cities we identified several factors that make for successful projects. We use these as criteria when vetting projects submitted for Chattalytics. We suggest you consider these factors when submitting your pitch for a Chattalytics project. You can view the success factors checklist here.

I'm ready. Let me pitch a Chattalytics project!

Fill in the form below to pitch a Chattalytics project. It is just that easy. We will be in be in touch soon!

Questions? Contact Us

423.643.7827 • performance@chattanooga.gov

This sounds amazing! How can I help?

If you are a...

Software developer/general tech geek who's been trying to utilize their talents for public good
College or graduate school student in a relevant field interested in getting real world experience
Business or organization who has the will and means to help solve these problems
City of Chattanooga employee that can help
Citizen who wants to get connected to City data

Then sign up with the form below to indicate your interest. We could always use a helping hand!

Office of Open Data and Performance Management

(423) 643-7827

tmoreland@chattanooga.gov