This article will give you tips on how to analyze responses from a Citizen survey about Pedestrian Safety using AI-powered tools and smart techniques for accurate, actionable insights.
Choosing the right tools for analysis
The way you approach survey analysis depends mainly on the data format. Quantitative data, such as the number of citizens who selected particular safety concerns or suggested specific changes, is straightforward. For numbers and simple charts, I rely on Excel or Google Sheets. They’re quick, flexible, and broadly understood.
Quantitative data: If you asked closed-choice questions—such as “How safe do you feel at crosswalks?”—handling the report is easy. You count up the results, calculate percentages, and create basic graphs. Good old spreadsheet tools are your friends here.
Qualitative data: If you received open-ended responses—like follow-up stories, suggestions, or qualitative descriptions—you’re dealing with a different animal. These insights are impossible to capture and summarize without help. You need AI tools to read, organize, and surface themes from mountains of text.
With qualitative survey data, there are two main approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
Copy and paste strategy: You can export your survey responses, paste them into ChatGPT, and ask it for summaries or themes. This works, but it’s not very convenient—especially with large datasets. It’s pretty fiddly managing the context window, and formatting issues often ruin your flow.
Manual overhead: You’ll need to clean and structure the text, feed manageable chunks to the model, and keep prompting it over and over for key findings. Larger datasets will always push up against the limits of what you and ChatGPT can handle at once.
All-in-one tool like Specific
Built for qualitative surveys: Tools like Specific's AI survey response analysis platform are purpose-built for this workflow. You can both collect Citizen surveys about Pedestrian Safety and analyze results in the same place—no copying, no headache.
Smart follow-ups: When citizens answer initial questions, Specific’s AI conducts follow-up probing automatically, which increases the completeness and quality of your data. See how this works with automatic AI follow-up questions.
Instant AI-powered insights: After you close your survey, Specific’s AI scans every response (and every follow-up), summarizes them, finds key themes, and gives you instant, actionable visual summaries. You can chat interactively with the AI about your data, adjusting filters, drilling down by topic or respondent group—all without ever opening a spreadsheet.
Fine-tuned analyst experience: Conversations with AI inside Specific are richer and more granular than anything you’d get by pasting data into vanilla ChatGPT, with more flexibility to manage which responses are included in your analysis. Learn more in the AI survey response analysis guide.
Useful prompts that you can use for analyzing Citizen survey response data about Pedestrian Safety
Let’s talk prompts. Good prompts turn a raw dump of survey responses into sharp, reliable findings that you can act on—or that you can use to inform public policy debates or infrastructure plans.
Prompt for core ideas: Use this to organize large volumes of text into crisp bullet-point themes. It’s a favorite prompt in Specific, and it works just as well in ChatGPT:
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
AI models always work better if you provide context. For example, before the core ideas prompt above, you could write:
"I ran a Citizen survey about Pedestrian Safety in a large urban area where 84% of pedestrian fatalities occur at non-intersection locations, mostly in dark conditions. My goal is to learn what changes citizens want to see, and where they feel most at risk."
Prompt for deep-dives: After extracting core ideas, follow up with:
"Tell me more about [core idea here]."
Prompt for specific issues: To check if people voiced particular concerns (such as larger vehicles in the city):
"Did anyone talk about SUVs or large vehicles? Include quotes."
Prompt for pain points and challenges: If you want to get a sense of citizen frustrations with local infrastructure or enforcement:
"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 sentiment analysis: To get a big-picture sense of how citizens feel about pedestrian safety:
"Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category."
Prompt for unmet needs & opportunities: Useful when you want ideas for future safety improvements:
"Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents."
Prompt for personas: If you’re planning targeted awareness or public engagement campaigns, ask for personas:
"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."
More detailed prompt inspiration is available in our guide to best questions for citizen survey about pedestrian safety.
How Specific analyzes qualitative survey data
When working with survey data about a topic like Pedestrian Safety, question type matters for analysis. Here’s how Specific breaks down the job:
Open-ended questions (with or without follow-ups): The system summarizes the core themes and ideas from all responses and automatically weaves in relevant details from follow-up answers. This makes it easy to see what issues or experiences are most commonly shared around pedestrian safety concerns, such as night visibility or intersection safety.
Choices with follow-ups: For questions like “What would make you feel safer as a pedestrian? (choose one),” Specific gives you a separate summary for each choice, reflecting only the related follow-up responses. That means you can instantly zoom in on, say, all responses from those wanting more crosswalks versus those who want lower speed limits.
NPS-style questions: With NPS (Net Promoter Score), which is often used to measure public satisfaction (learn more about NPS setups here), Specific summarizes the opinions and experiences of promoters, passives, and detractors separately. This highlights specific improvements needed for each group.
You can perform similar analyses in ChatGPT, but it’s more labor intensive—you’ll need to be diligent about organizing your data and carefully tailoring your prompts for each scenario.
Overcoming AI context size limits when analyzing large Citizen survey datasets
Whenever you analyze survey data with AI, context window size can become a bottleneck. If your Citizen survey about Pedestrian Safety collected a flood of detailed responses, you’ll hit the upper limit of what the AI can process in a single go.
Specific offers two important features to tackle this:
Filtering: You can filter conversations so only the responses where users addressed selected questions—or chose specific answers—are included in the AI’s analysis. This reduces context size and tightens relevance.
Cropping: Cropping allows you to select which questions get sent to the AI for analysis, ensuring that even if you have hundreds of respondent conversations, all the answers are focused only on the points you care about (for example, “nighttime walking safety” or “dangerous intersections”).
This flexibility means you won’t miss out on insights, even when dealing with extensive, nuanced qualitative feedback—as is common in city safety surveys. For a step-by-step on advanced survey creation, check this how-to guide for creating citizen surveys about pedestrian safety.
Collaborative features for analyzing Citizen survey responses
Collaborating on survey analysis about Pedestrian Safety can quickly get messy when you have multiple stakeholders—city planners, community activists, or transportation officials—each wanting to interpret and discuss the results.
Seamless AI chat analysis: In Specific, you can analyze your Citizen survey data collaboratively just by chatting with the AI. There’s no need to export, import, or juggle files between teams. Insights are available for everyone on your project.
Multiple, filterable chats: Specific lets you spin up multiple chats, each with custom filters (such as location, age, or survey response content), and every chat is labeled with its creator. This makes it obvious who’s exploring which part of the survey, and why.
Clear attribution: When collaborating, each message within the AI chat shows the sender’s avatar and name. This reduces confusion and keeps everyone aligned, especially with cross-functional teams. You can even branch off new lines of investigation, saving everyone’s time and sanity.
Collaborative analysis features make Specific especially valuable for teams working on complex community issues, where consensus must be developed through open, transparent insight-sharing.
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