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How to use AI to analyze responses from citizen survey about parks and recreation

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Adam Sabla

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Aug 22, 2025

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This article will give you tips on how to analyze responses from a citizen survey about parks and recreation, using AI survey analysis methods for practical results.

Choosing the right tools for survey response analysis

The best approach and tools for analyzing survey responses depend on the form and structure of your data. Here’s how you can make sense of what’s in front of you:

  • Quantitative data: Numbers—like how many people prefer a certain park facility or chose a specific option—are straightforward. I usually open Excel or Google Sheets to run counts, filter responses, and visualize trends. Classic spreadsheet tools get the job done for basic quantitative questions.

  • Qualitative data: Open-ended answers and follow-up responses are where it gets tricky. If you’re reading dozens or hundreds of conversations, it’s impossible to process everything manually. You need AI tools that can read, recognize patterns, summarize, and help you dig deeper.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

Copy-paste export: You can export your data (chat logs, CSV, or plain text) and paste it into ChatGPT. From there, you can chat about your data—summarizing, asking for themes, or getting quick overviews. It’s flexible, and works well for smaller datasets.

Not very convenient for scale: As soon as you have a long survey or lots of responses, you run into context size limits. Organizing what’s fed into GPT, filtering to specific questions, and keeping track of prompts is all manual. It’s powerful, but gets unwieldy fast.

All-in-one tool like Specific

Purpose-built for qualitative survey analysis: Specific is designed for AI-powered survey collection and analysis, so you get the best of both worlds. When you collect responses with Specific, it asks dynamic follow-up questions, raising the quality and depth of each answer. Here’s how automatic AI follow-up questions work.

Instant summary and insights with AI: With AI-powered analysis in Specific, I get summaries, core themes, and actionable insights on the spot—whether I'm looking at open-ended questions, choice questions with followups, or even NPS scores. There’s no manual exporting or spreadsheet headaches. I just chat with the data, the same way I'd use ChatGPT, but it’s tailored for survey conversations.

Manage your data contextually: Specific lets you organize your chats, apply filters, and manage exactly what data the AI analyzes—making deep dives much more practical if you want to iterate with your collaborators.

If you're curious about how to create surveys that work well for this kind of analysis, check out these tips for asking the right questions in citizen surveys about parks and recreation.

Useful prompts that you can use for citizen survey response analysis about parks and recreation

Prompts are your shortcut to high-quality insights. Below are some favorites that work for parks and recreation surveys with citizens. You can use these directly in tools like ChatGPT or in Specific’s built-in AI chat.

Prompt for core ideas: This gets you right to the key topics echoed across your dataset. Ideal when you're staring at a wall of open-ended answers.

Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.

Output requirements:

- Avoid unnecessary details

- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top

- no suggestions

- no indications

Example output:

1. **Core idea text:** explainer text

2. **Core idea text:** explainer text

3. **Core idea text:** explainer text

Context matters: AI always delivers better insight when you add survey context. Be explicit about goals, audience, and focus—the more you share, the more relevant your analysis becomes.

I conducted this survey with citizens of [my city] to understand how they use public parks and what barriers prevent them from visiting. My main goal is to improve accessibility and engagement, especially for families with kids. Analyze key drivers and challenges.

Dive deep into a theme: If you see a core idea and want more depth, just ask: “Tell me more about [core idea]”. The AI will expand with examples, quotes, or patterns.

Prompt for specific topic: Use this to quickly verify if anyone brought up something you care about (for example, safety, walking trails, events, or accessibility):

Did anyone talk about improved lighting in parks? Include quotes.

Prompt for personas: This is great if you want to visualize who your respondents are—not just in demographics, but in attitudes and needs.

Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.

Prompt for pain points and challenges: Get a list of the most common frustrations or barriers your city faces regarding parks and recreation. You’ll spot recurring issues faster:

Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.

Prompt for motivations & drivers: Uncover the “why” behind behaviors. This usually points to what people value most about their parks—and what motivates them to visit or avoid them.

From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.

Prompt for suggestions & ideas: Ideal for surfacing creative proposals or community-driven solutions.

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

If you need inspiration on how to create surveys with these types of questions or themes, you can try an AI-powered survey builder for citizen parks and recreation surveys.

How Specific handles analysis for different question types

With Specific, analysis adapts to question type. For citizen parks and recreation surveys, this is a game changer for quickly unpacking detailed insights:

  • Open-ended questions (with or without followups): Specific generates a summary for all main answers, plus follow-up responses tied to each question. You see core themes, explanations, and highlights—all in one view.

  • Multiple choice with followups: Each choice (e.g., “Most visit for playgrounds,” “Prefer hiking”) has its own breakdown of follow-up responses, so you can compare what’s driving each group’s selection. If you want to structure your survey for this, consider using an AI survey editor for easy question creation.

  • NPS questions: Responses are grouped by detractors, passives, and promoters. You get summaries of follow-up answers in each category—a powerful way to see why satisfaction lags or what boosts loyalty.

You can absolutely do this in ChatGPT as well—it just takes more manual set-up: exporting, filtering, and prompting for each group or question type.

How to tackle the AI context limit in survey analysis

The reality is, even the best AI models have limits to how much they can “see” at once. This is a major consideration with parks and recreation surveys, where lots of citizens respond, often in detail.

Specific offers out-of-the-box solutions for this challenge:

  • Filtering: Instead of analyzing everything, I can filter conversations so only those where respondents answered selected questions (like an open-ended question about recreation barriers) or made specific choices (like “uses playgrounds most”) are included. This keeps the dataset focused and within the AI’s processing window.

  • Cropping: I can select just the questions that matter for a given analysis—maybe only NPS followups or comments on new programs. This means more responses fit into the analysis and insights stay sharp.

If you want to see this in action, check out how AI survey response analysis works in Specific.

Collaborative features for analyzing citizen survey responses

Collaboration is tough: Analyzing responses from large-scale parks and recreation surveys often means working with others—policy staff, city planners, researchers. Emailing spreadsheets or chat logs back and forth is slow and messy.

AI chat for teams: Specific lets me analyze survey data by chatting with AI, and each conversation (chat) can have its own filters and context (“focus on families,” “talk only about accessibility,” etc.). It keeps everything organized while highlighting who started each chat—no accidental overwrites or duplicate work.

Clarity about who said what: In every AI chat, I can see exactly who said what, with avatars. It’s immediately clear which colleague contributed which idea or question, making discussion much more seamless for city teams or researcher groups.

Multiple perspectives, zero chaos: This collaborative structure makes it easy for teams to slice and dice citizen feedback from parks and recreation surveys—without confusion or losing context. If you want to build the right survey workflow from the start, you can check out a detailed guide to creating these citizen surveys.

Create your citizen survey about parks and recreation now

Launch a citizen parks and recreation survey that’s simple to create, collects meaningful responses with AI follow-ups, and instantly analyzes results for fast decision-making.

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Try it out. It's fun!

Sources

  1. National Recreation and Park Association. Local Parks and Recreation Engagement Report (2022)

  2. Frontiers Sustainable Cities. “Trends in Community Park Usage: Age Group Analysis from Tokyo, Japan”

  3. National Institutes of Health / PMC. “Factors Associated with Refusal Rates in Park-User Surveys”

  4. Zipdo. “Artificial Intelligence in the Outdoor Industry: Statistics and Insights”

  5. Wifitalents. “AI in the Theme Park Industry: Key Data and Forecasts”

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.