In Action
Playground renovation project - a “beloved amenity, real safety tradeoffs” decision in Naples, FL:
Should Naples preserve the spirit of a community-built playground while modernizing it for safety and accessibility? Rather than letting the outcome be shaped by whoever calls most, emails most, or shows up loudest, the City ran a Scientific Survey to capture where residents actually drew the lines (keep vs. replace, safety expectations, design priorities, and what tradeoffs felt acceptable).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on overall direction, the conditions people care about, and the concerns that need to be addressed in project messaging and decisions. The trust moment came fast—after results were shared, even a frequent caller who usually complained flipped to calling City Hall to compliment the Communications Director. When the community can see the results and the City can point to measured input, the conversation shifts from heated assertions to clear, workable next steps.
Streets and sidewalks - a “daily frustration, real budget tradeoffs” decision in North Ogden, UT:
Where should the City prioritize street and sidewalk work—and what fixes matter most to residents? Rather than letting the agenda be shaped by whoever submits the most complaints (or by the handful of people who show up to speak), North Ogden ran a Scientific Survey to capture where residents actually drew the lines (safety hotspots, maintenance priorities, connectivity gaps, and what people want fixed first).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what improvements residents feel most urgently, how priorities vary across the community, and where the City can make choices that residents will recognize as fair. When the community can see the results and the City can point to measured input, the conversation shifts from competing anecdotes to clear, defensible prioritization.
Ice rick operations - a “community tradition, real facility tradeoffs” decision in Saugeen Shores, ON:
How should the Town run its ice rinks—so they stay accessible, well-used, and financially responsible? Rather than letting rink decisions get driven by the loudest voices (or the most frequent users alone), Saugeen Shores ran a Scientific Survey to capture where residents actually drew the lines (hours and scheduling priorities, programming vs. drop-in, user fees, maintenance expectations, and what “good service” looks like).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what residents value most about rink access, which tradeoffs they’ll accept, and what changes would feel fair across different types of users. When the community can see the results and the Town can point to measured input, the conversation shifts from competing demands to clear, workable operating direction.
Possible sales tax increase — a “service stability, taxpayer trust” decision in Duarte, CA:
Should Duarte pursue a sales tax increase—and if so, how should the City use the revenue? Rather than letting a high-stakes funding question be shaped by whoever emails most or shows up loudest, Duarte ran a Scientific Survey to capture where residents actually drew the lines (support/opposition, priority uses, accountability expectations, and what outcomes would justify the increase).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on whether there’s a viable path to support, what spending categories residents care about most, and what safeguards or reporting residents expect in return. When the community can see the results and the City can point to measured input, the conversation shifts from tax anxiety and speculation to clear, defensible choices.
Backyard chicken ordinance - “small policy, big feelings” decision in Stillwater, OK:
Should backyard chickens be allowed—and under what rules. Rather than letting the outcome be shaped by whoever emails most or shows up to speak, the City ran a Scientific Survey to capture where residents actually drew the lines (yes/no, limits, nuisance concerns, enforcement expectations).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on overall support, the conditions people care about, and the concerns that need to be addressed in the ordinance language. When the community can see the results and the City can point to measured input, the conversation shifts from neighbor-to-neighbor friction to clear, workable policy choices.
Affordable housing - “big stakes, hard tradeoffs” decision in Placer County, CA:
What do residents need most in housing—and what’s driving costs in their communities? Rather than letting the narrative be shaped by the most persistent emails or the handful of voices who show up to speak, the County ran a Scientific Survey to capture where residents actually drew the lines (cost pressures, housing types/availability, priority needs, and the tradeoffs people are willing to accept).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what housing problems feel most urgent, what solutions residents prioritize, and where concerns or constraints need to be addressed in policy and communications. When the community can see the results and the County can point to measured input, the conversation shifts from competing anecdotes to clear, defensible direction.
Short-term rental compliance - “quality of life, neighborhood trust” decision in Ogden, UT:
How should the City deal with short-term-rental nuisances—without punishing responsible operators? Rather than letting the outcome be shaped by whoever complains most (or whoever has the most to gain), Ogden ran a Scientific Survey to capture where residents actually drew the lines (noise, parking, trash, party houses, enforcement expectations, and what rules feel fair).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on how widespread the nuisance problem feels, which impacts matter most, and what enforcement or policy approaches residents will accept. When the community can see the results and the City can point to measured input, the conversation shifts from neighbor-vs.-neighbor frustration to clear, workable guardrails.
Recyclable materials - a “good intentions, real contamination tradeoffs” decision in Arlington, TX:
What materials should go in recycling—and how should the City reduce confusion and contamination? Rather than letting the rules be dictated by rumor, habit, or the loudest complaints, Arlington ran a Scientific Survey to capture where residents actually drew the lines (what people think is recyclable, what they’re currently doing, where the confusion is, and what guidance would change behavior).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on knowledge gaps vs. willingness, the specific materials causing the most uncertainty, and the messaging residents will actually follow. When the community can see the results and the City can point to measured input, the conversation shifts from wish-cycling and frustration to clear, workable rules people can follow.
Park funding bond measure - a “shared outdoor life, real tax and bond tradeoffs” decision in Gig Harbor, WA:
Should Gig Harbor pursue a bond measure to fund parks—and what should that investment deliver? Rather than letting a ballot-level funding question be shaped by whoever shows up loudest or whoever is most organized, Gig Harbor ran a Scientific Survey to capture where residents actually drew the lines (support/opposition, priority projects, willingness to pay, and the accountability residents expect in return).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on whether there’s a viable path to support, what park outcomes residents value most, and what safeguards or reporting make the funding feel earned. When the community can see the results and the City can point to measured input, the conversation shifts from bond-measure speculation to clear, defensible choices.
Downtown Franklin Street — a “local character, real access tradeoffs” decision in Chapel Hill, NC:
What changes would make downtown Franklin Street work better—for businesses, residents, and visitors? Rather than letting the conversation be shaped by whoever complains loudest or shows up most often, Chapel Hill ran a Scientific Survey to capture where residents actually drew the lines (walkability and safety, parking and loading, traffic flow, public space feel, and what improvements matter most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what people want preserved vs. changed, which tradeoffs the broader community will accept, and what concerns need to be addressed in design and messaging. When the community can see the results and the Town can point to measured input, the conversation shifts from downtown debate and anecdotes to clear, workable direction.
Growth - a “small-town character, inevitable change” decision in De Soto, KS:
How fast should De Soto grow—and what should that growth look like on the ground? Rather than letting the direction be set by whoever shows up loudest (pro-growth or anti-growth), the City ran a Scientific Survey to capture where residents actually drew the lines (pace of growth, housing vs. commercial mix, infrastructure expectations, and what needs to come first to keep growth livable).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on the community’s appetite for growth, the conditions residents attach to it, and the concerns that need to be addressed in planning and communications. When the community can see the results and the City can point to measured input, the conversation shifts from growth anxiety and speculation to clear, workable direction.
Homelessness strategic plan - a “compassion and safety, real-world constraints” decision in Concord, CA:
What should the City prioritize in its homelessness strategy—and what approaches feel both effective and fair? Rather than letting the plan be shaped by the most visible incidents or the loudest public comments, Concord ran a Scientific Survey to capture where residents actually drew the lines (service and shelter priorities, public space concerns, prevention vs. response, and what outcomes residents want the plan to deliver).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what residents most want solved, which strategies earn the broadest support, and where concerns need to be addressed in the plan’s language and implementation. When the community can see the results and the City can point to measured input, the conversation shifts from frustration and polarization to clear, workable priorities.
Recreation options (Pickleball) - a “popular new sport, shared-space tradeoffs” decision in Lake Forest, CA:
How should Lake Forest expand recreation options—especially pickleball—while keeping parks usable for everyone? Rather than letting the outcome be shaped by whoever complains most (about noise, crowding, or court access) or whoever shows up loudest, the City ran a Scientific Survey to capture where residents actually drew the lines (demand for courts, preferred locations, noise/time expectations, and what recreation investments matter most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on how broad pickleball demand really is, what guardrails residents view as fair, and which recreation tradeoffs the community will accept. When the community can see the results and the City can point to measured input, the conversation shifts from court conflict and anecdote to clear, workable recreation planning.
Water tower project - a “community identity, real infrastructure tradeoffs” decision in Bedford, TX:
What should the City do with the water tower—and what option best fits Bedford’s priorities? Rather than letting the direction be shaped by whoever emails most or shows up loudest, Bedford ran a Scientific Survey to capture where residents actually drew the lines (preserve vs. replace, cost sensitivity, aesthetics/identity, and what outcomes matter most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on which option has broad support, what people are willing to pay for (or not), and the concerns that need to be addressed in project messaging and decision language. When the community can see the results and the City can point to measured input, the conversation shifts from symbolic debate and guesswork to clear, defensible direction.
Bicycle park - a “youth recreation, neighborhood noise concerns” decision in Pacifica, CA:
Should Pacifica move forward with a bike park—and what guardrails would make it workable for nearby neighbors? Rather than letting the outcome be shaped by whoever shows up loudest (pro-amenity or anti-noise), the City ran a Scientific Survey to capture where residents actually drew the lines (support/opposition, noise and hours expectations, location sensitivity, and what mitigations matter most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on how broad support really is, which concerns are most widely shared vs. localized, and what conditions residents view as fair. When the community can see the results and the City can point to measured input, the conversation shifts from NIMBY conflict and speculation to clear, workable site and operating rules.
Downtown — a “revitalization and pride, real change tradeoffs” decision in Fairfield, CA:
What should Fairfield prioritize to strengthen downtown—and what changes would residents most want to see? Rather than letting the vision be shaped by a few loud voices or anecdotal complaints, Fairfield ran a Scientific Survey to capture where residents actually drew the lines (safety and cleanliness, business mix, events and amenities, parking/access, and the overall downtown experience).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what’s holding downtown back, which improvements have broad support, and what concerns need to be addressed in strategy and messaging. When the community can see the results and the City can point to measured input, the conversation shifts from competing narratives to clear, workable direction.
Budget - a “limited dollars, high expectations” decision in North Richland Hills, TX:
What should the City prioritize in the budget—and where do residents most want to see investment or restraint? Rather than letting the budget conversation be shaped by whoever emails most or who has the time to show up, North Richland Hills ran a Scientific Survey to capture where residents actually drew the lines (service priorities, tradeoffs across departments, willingness to pay, and what outcomes matter most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what rises to the top when residents have to choose, where there’s broad alignment vs. real disagreement, and which concerns need to be addressed in budget messaging and final allocations. When the community can see the results and the City can point to measured input, the conversation shifts from assumptions and competing wish-lists to clear, defensible priorities.
City communications - a “information overload, trust and reach” decision in Beavercreek, OH:
How should the City communicate so residents actually receive, understand, and trust what matters? Rather than relying on assumptions (“everyone’s on Facebook” / “nobody reads email”) or a handful of anecdotal complaints, Beavercreek ran a Scientific Survey to capture where residents actually drew the lines (preferred channels, frequency, emergency vs. everyday updates, and what content residents want most).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on which channels truly reach people, how different groups want to be contacted, and what changes would most improve clarity and confidence. When the community can see the results and the City can point to measured input, the conversation shifts from guesswork and scattered feedback to a communications plan built on what residents say they’ll use.
Johnson Property - a “once-in-a-generation land use, real community tradeoffs” decision in Natick, MA:
What should Natick do with the Johnson Property—and what outcomes should guide the site’s future? Rather than letting a high-stakes, long-horizon decision be shaped by whoever has the time to show up (or the strongest single-issue push), Natick ran a Scientific Survey to capture where residents actually drew the lines (preferred uses, preservation vs. development balance, impacts on traffic/neighbors/taxes, and what “success” looks like for the community).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on the community’s directional consensus, the conditions residents attach to different options, and the concerns that need to be addressed in planning language and public communication. When the community can see the results and the Town can point to measured input, the conversation shifts from speculation and polarization to clear, workable criteria for next steps.
Cultural priorities - a “identity and belonging, real funding tradeoffs” decision in Watertown, MA:
What should Watertown prioritize in arts and culture—and what investments would residents value most? Rather than letting the agenda be shaped by the most connected stakeholders or whoever shows up to meetings, Watertown ran a Scientific Survey to capture where residents actually drew the lines (programming vs. facilities, access and inclusion, community events, partnerships, and how to allocate limited resources).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what cultural outcomes matter most, which ideas have broad support, and what concerns need to be addressed in planning and budgeting. When the community can see the results and the Town can point to measured input, the conversation shifts from competing wish-lists to clear, defensible priorities.
Downtown events — a “local fun and vitality, real noise and disruption tradeoffs” decision in Georgetown, TX:
What kinds of downtown events should Georgetown support—and how should the City manage impacts on nearby residents and businesses? Rather than letting the event calendar be shaped by whoever complains most (or whoever advocates loudest), Georgetown ran a Scientific Survey to capture where residents actually drew the lines (event types and frequency, hours and noise expectations, parking/traffic impacts, and what rules feel fair).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on how much appetite there is for events, which impacts are most important to manage, and what guardrails residents will accept. When the community can see the results and the City can point to measured input, the conversation shifts from downtown tension and anecdote to clear, workable event policy choices.
Master plan - a “future vision, real growth tradeoffs” decision in Chocolay Township, MI:
What should Chocolay prioritize in its master plan—and what kind of future do residents want to build toward? Rather than letting long-range planning be shaped by the handful of people who always show up (or by the loudest single issue), Chocolay Township ran a Scientific Survey to capture where residents actually drew the lines (preservation vs. development, housing and infrastructure needs, community character, and what investments should come first).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on the community’s shared priorities, the conditions residents attach to change, and the concerns that need to be addressed in the plan’s language and implementation. When the community can see the results and the Township can point to measured input, the conversation shifts from speculation and competing visions to clear, workable direction.
Fire and police services - a “public safety expectations, real staffing tradeoffs” decision in Canyon, TX:
What matters most to residents in fire and police services—and where should the City focus improvements? Rather than letting public safety priorities be shaped by whoever speaks up most (often after a bad experience), Canyon ran a Scientific Survey to capture where residents actually drew the lines (service satisfaction, response and coverage expectations, visibility and prevention, and what investments feel most justified).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what residents value most in public safety, where confidence is strong vs. where concerns are emerging, and what changes the community would recognize as meaningful. When the community can see the results and the City can point to measured input, the conversation shifts from isolated anecdotes to clear, defensible priorities.
Economic development — a “local jobs and vitality, real growth tradeoffs” decision in Citrus Heights, CA:
What kind of economic development should Citrus Heights pursue—and what changes would residents most want to see? Rather than letting the vision be shaped by a handful of loud inputs or assumption-driven planning, the City ran a Scientific Survey to capture where residents actually drew the lines (business mix, corridor revitalization priorities, quality-of-life impacts, and what “success” should look like).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what residents want more of (and less of), which priorities have broad support, and what concerns need to be addressed in strategy, zoning, and messaging. When the community can see the results and the City can point to measured input, the conversation shifts from competing visions to clear, workable direction.
Downtown parking — a “easy access for shoppers, real curbspace tradeoffs” decision in Keene, NH:
What changes would make downtown parking work better—for customers, employees, and nearby residents? Rather than letting the issue be defined by whoever complains most (or by anecdotal “I can never find a spot” stories), Keene ran a Scientific Survey to capture where residents actually drew the lines (time limits, pricing vs. free parking, employee parking needs, turnover for businesses, and enforcement expectations).
This is a strong demo example because it shows how FlashVote helps staff separate preference from intensity: you get a clean read on what’s actually driving parking frustration, which solutions residents will accept, and what tradeoffs feel fair across different downtown users. When the community can see the results and the City can point to measured input, the conversation shifts from parking drama and assumptions to clear, workable parking policy choices.
