How smart campaigns use 2024 census data and geospatial analysis to identify the exact neighborhoods where elections are won and lost
Picture this: It’s three weeks before election day. Your campaign has spent heavily on TV and social media ads blanketing the entire district, deployed volunteers to knock on doors in “safe” neighborhoods, and placed field offices based on gut feelings about where your supporters might live.
On election night, you lose by a narrow margin.
The painful truth? The votes you needed were sitting in specific neighborhoods that your data could have identified months ago. Neighborhoods where targeted investment in door-knocking and local outreach would have delivered victory.
This scenario plays out in countless races because campaigns are still fighting with 20th-century tools in a 21st-century information environment.
Every political strategist faces the same brutal reality: limited resources, unlimited places to spend them.
The questions that truly keep them staring at the ceiling at 3 AM:
These aren’t abstract strategic questions—they’re survival questions. Get them wrong, and you’re updating your LinkedIn profile the day after the election.
Connectivity in Uriangato (Guanajuato) from under 20 % (red) to 100 % (green), 3D reflecting population
Most campaigns approach voter targeting like they’re painting with a house brush when they need a fine-tip pen.
The Old Way: Broad demographic assumptions spread across entire ZIP codes or counties
The Problem: This approach treats every voter in a ZIP code as identical and misses the granular reality of how people actually live and vote.
A single ZIP code might contain:
Painting them all with the same brush wastes money and misses opportunities.
Internet accessibility changes hugely among neighborhoods in the main cities of Guanajuato
Here’s what changes everything: Section-level precision using the most latest census data combined with actual electoral performance.
While your competitors make broad demographic assumptions, you identify the exact neighborhoods where small increases in turnout or persuasion will win elections.
This isn’t about having more data—it’s about having actionable intelligence at the geographic scale where campaigns actually operate: the neighborhood level.
Campaign Return of Investment (ROI) Potential by Electoral Section: Strategic resource allocation analysis combining voter volume with electoral competitiveness to identify maximum impact zones (greener the better) for campaign investment.
When you combine the latest census data with electoral results at the granular section level, three critical insights emerge:
1. High-Impact Zones
Areas with favorable demographics but low historical turnout—your opportunity zones. Neighborhoods full of people who should support you but aren’t showing up to vote.
2. Swing Territories
Neighborhoods where small shifts in turnout could flip results. These aren’t precincts that always go 70-30 one way—they’re the 52-48 areas where your ground game makes the difference.
3. Resource Allocation Sweet Spots
The precise locations where your limited budget will have maximum impact—where to place field offices, run local ads, and deploy ground teams.
High and very high priority neighborhoods: areas with low voter turnout (2024) and high potential for electoral swing
Think about the difference between a general surgeon and a neurosurgeon. Both are skilled doctors, but when you need brain surgery, precision matters more than broad knowledge.
Campaign targeting works the same way.
Broad-brush consultants tell you: “Focus on suburban college-educated voters aged 35-55”
Geographic intelligence analysis tells you: “Focus on sections 7, 12, and 23 in Ward 4, where college-educated turnout dropped 8% in the last election but new resident data shows significant favorable demographic shifts”
That’s the difference between strategy and surgery.
The presidential electoral results (2024) in City of Celaya, red for higher rates of support for Morena and blue for higher voter gains from the oposition.
How granular demographic analysis reveals the neighborhoods that decide close elections
When political strategists look at Mexico’s growing urban centers, they typically see broad demographic categories: “working-class industrial cities,” “middle-class growth areas,” or “traditional party strongholds.”
But elections aren’t won by demographics—they’re won by neighborhoods.
Consider what section-level analysis might reveal in a competitive race where the margin of victory is just a few percentage points across an entire state.
In any close election, the margin of victory often comes down to a relatively small number of votes concentrated in specific, identifiable neighborhoods.
But here’s what should make every campaign strategist’s stomach drop: These decisive neighborhoods can be identified through competent analysis, yet most campaigns never find them.
Imagine being the losing candidate’s campaign manager, sitting in that post-election debrief:
Now imagine discovering that the votes you needed were concentrated in specific sections that your campaign never properly identified or targeted.
Competitiveness score among the high priority neighborhoods: darker red areas show where election results (2024) were closest to a 50-50 split between MORENA and opposition parties
How do you explain that to your candidate?
“Well, we knew those neighborhoods were changing, but we treated the entire city as a single demographic instead of identifying the sections where new residents had moved in…”
“Yes, we saw the city had young voters, but we spread our youth outreach across dozens of sections instead of concentrating on the areas where they actually live…”
This isn’t just about missing an opportunity. This is about active waste of resources that could have changed the outcome.
While campaigns spend heavily in already-decided neighborhoods, use wrong messaging for transformed demographics, and spread resources thin across low-opportunity areas—the decisive votes sit waiting in neighborhoods that proper analysis could identify.
Deciding on the outreach method is highly relevant because of the different internet access and mobile phone internet access coverages across the municipalities (besides the suburban zones)
Questions I’d recommend any campaign manager to be thinking:
If any of those questions made you uncomfortable, you understand why section-level analysis isn’t optional anymore—it’s survival.
Now imagine the opposite scenario. Imagine being the campaign manager who:
Instead of missing decisive votes, you go after them. That’s the difference between broad-brush guessing and surgical precision targeting. And at the end of the day, that’s the difference between explaining a loss and celebrating a victory.
Instead of spreading field offices across broad geographic areas, campaigns can concentrate efforts in identified opportunity zones with maximum impact potential.
And rather than city-wide messaging, campaigns can deploy neighborhood-specific communication strategies based on actual demographic realities.
In addition, door-knocking and volunteer deployment can follow section-level intelligence:
This approach isn’t just about one election in one region. It’s a blueprint for how every campaign can stop wasting money on broad assumptions and start investing in surgical precision.
Ready to transform your campaign targeting from guesswork to precision? The neighborhoods that will decide your next election are out there, waiting to be discovered. The question is: Will you find them before your opponent does?
Data Sources and Disclaimer:
This analysis utilizes official data from the Instituto Nacional de Estadística y Geografía (INEGI) Censo de Población y Vivienda 2020 for demographic indicators, and electoral results from the Instituto Nacional Electoral (INE) corresponding to the 2024 presidential election. All geographic boundaries and administrative divisions are based on official INEGI cartographic products.
Disclaimer: This analysis is provided for educational and informational purposes only. The findings, interpretations, and strategic recommendations presented herein represent analytical exercises based on publicly available data and do not constitute political advice or endorsements. The author assumes no responsibility for decisions, actions, or outcomes resulting from the use of this analysis. Users are advised to conduct their own due diligence and consult appropriate legal and political advisors before making strategic decisions.