The Texas Challenge
The Texas Challenge
Texas is the second largest state in the U.S. by both population and physical size, and the number of people, languages, cultures, religions, and landscapes is difficult to comprehend. One thing Texas cities of all sizes do have in common, despite the diversity in the state, is that they're currently facing similar challenges. In this report, we discuss three of the top challenges Texan city managers are grappling with and how AI technology can provide innovative solutions to overcome the issues posed by (1) Senate Bill 2; (2) rapid growth; and (3) affordability and homelessness.
The phrase “everything’s bigger in Texas” is an understatement. As the second largest state in the United States by both population and physical size, the number of people, languages, cultures, religions, and landscapes is difficult to comprehend. As I walked around the Texas City Management Association Annual Conference in Fort Worth last week chatting with City Managers, I began to discover trends in the conversations I was having. It seemed that despite the diversity in the state, cities of all sizes in Texas are struggling with some of the same challenges. The good news is that thanks to AI technology, solutions to these challenges are much closer than one might think.
Senate Bill 2: Texas Tax Cap
Anyone in any government position in Texas is well aware of Senate Bill 2. In short, the bill requires voter approval before governments can increase their property tax revenue by more than 3.5%. The fear is that this bill will severely limit the budget of many cities, thus decreasing public safety and the ability of a city to provide additional government services.
Cities are well known for being understaffed and underfunded, yet a bill like this forces cities to grapple with a very big challenge: how do governments, with extremely limited budgets, allocate resources?
Resident feedback is, or at least should be, a key component when planning how to allocate budget to different city services – and ever more so when resources are becoming increasingly limited. After all, if a city spends millions on projects that don’t address the real needs or priorities of its residents, not only can this backfire on the administration, it’s also money wasted. In traditional resident feedback collection, cities use expensive surveys or council meetings to understand the sentiment of residents. Yet these methods aren’t in real time and are biased. By processing millions of resident feedback data points using AI, cities can quickly understand the views of residents and which services they like. This allows for fiscal decisions based on real data about how residents want and need their cities to spend limited budgets.
A great example is Modi’in, Israel, which opened a 1400 acre multi-use, public space park in 2010. With an annual budget of $1 million, the park quickly became a centerpiece for the community. Yet when the City went to understand the park’s use and prioritize budget for its future, it was stumped. Traditional surveys and council meetings didn’t provide feedback on the park. Modi’in turned to Zencity. Using AI, Modi’in was able to automatically aggregate and process thousands of city-wide resident-generated data points. The AI categorized, sorted, and analyzed the feedback, the sentiment of the conversations, and trends. The City was able to understand which services residents love, which services to optimize, and which services to stop funding. This saved the city a lot of money and helped better prioritize its limited resources.
Texas is home to some of the fastest growing cities in the United States. Some city managers I spoke with mentioned 200% growth year-over-year. While this growth is exciting and promising for many cities, it’s also a massive challenge to handle. Budget size doesn’t usually grow proportionally with city size, and the influx of new neighborhoods, businesses, and people require a lot of services. Furthermore, it’s near impossible to get feedback from all these new residents to influence the decisions of the city.
AI can help in a few concrete ways:
- Data Collection: A survey performed 6 months ago in a city with 200% growth year-over-year is completely invalid. Cities are forced to stay up-to-date with new sources that are constantly evolving. Add to this the fact that residents are talking about the city in so many different places and it’s a complete nightmare. A Communications team can never possibly monitor each source nor stay up to-do-date with the new ones. Thanks to AI, all these sources – social media, digital news, offline TV and radio, 311 / CRM, Contact Us forms, e-mails, etc. – can be combined into one central place. This saves hundreds of hours of manual labor.
- Citizen Sentiment: When cities grow very fast, two groups develop: residents that have lived in the city for years, and new residents to the area. Often their views clash, creating a major challenge for the city. How does the city keep up-to-date on the constantly changing views of its residents overall? With AI, millions of data points can be processed in the click of a button and in real time. This allows cities to understand changing trends, what residents want, and their sentiment towards key issues.
- Geolocation: As a city grows and the population diversifies, different parts of the city evolve in different ways. This can give rise to a new challenge of understanding residents’ needs by area. Geo-location technology, paired with AI, can help break down information by neighborhood so local government leaders can best allocate resources. Picture having the ability to understand that one neighborhood requires sidewalk repair while another neighborhood requires garbage pickup. The city will save time, energy, and money directing services to the appropriate residents, rather than a one-size-fits-all approach.
Affordability and Homelessness
As populations grows, and the ability to provide additional services due to budget cuts decreases, affordability and homelessness are increasingly becoming a big concern to Texas cities. It’s no surprise that the student activity at TCMA centered on these topics. Yet what can be done to understand how these issues are impacting residents? How can the city take more concrete steps to reduce homelessness and make cities more affordable? AI can help.
With traditional quantitative measures, a city can understand how many individuals sleep in a homeless shelter or what’s the year-over-year average housing price increase. What’s missing from this equation is the qualitative component, primarily: do residents notice the impact of the city’s efforts on curbing homelessness. Using AI and a sentiment analysis model, cities can begin to understand to what degree their policies are impacting residents. Does the city feel safer? Are residents talking about homelessness in certain areas more than in others?
Beaverton, Oregon used AI and Zencity to successfully launch a Safe Parking program. How would those neighborhoods most impacted – the pilot sites of the Safe Parking program – respond to the initiative? How would the community as a whole respond? The City tracked three months of resident feedback data from the planning process through the initial launch. Using geo-location capabilities, the city followed the sentiment of residents in each neighborhood and understood that not every neighborhood is the same. Different communities had different types of feedback, and thus the city could respond accordingly. By addressing concerns by neighborhood and keeping the pulse of the community on this sensitive topic, the city succeeded in launching a controversial pilot with support of residents and local businesses alike. You can read the full case study on Beaverton’s success here.
The Lone Star State
Texas might be the Lone Star State, yet the good news for Texas cities is that they don’t have to tackle these challenges alone. Cities and the residents they serve deserve the best, and thanks to AI-driven city-specific technology, Texas cities can now better understand the needs of their residents in real time and translate that data into stronger decision-making for overall better cities.