City Quality-of-Life Composite Score
Build your own weighted quality-of-life index. Combine seven dimensions of city livability with custom weights to match your priorities.
Build Your Custom Quality-of-Life Index
Select one or two cities and adjust the dimension weights to reflect what matters most to you. Get a personalized composite score based on your priorities.
Choose Cities
Enter one or two US cities to compare
Adjust Weights
Prioritize what matters to you
Dimensions: Housing Affordability, Job Opportunity, Commute Ease, Transit Access, Climate Comfort, Safety, Amenities
Understanding City Quality-of-Life Composite Score: Custom Weighted Index
Quality of life is subjective and varies based on individual priorities, lifestyle preferences, and life circumstances. The City Quality-of-Life Composite Score helps you build your own personalized quality-of-life index by combining seven key dimensions of city livability—housing affordability, job opportunity, commute ease, transit access, climate comfort, safety, and amenities—with custom weights that reflect your priorities. This tool enables students, professionals, researchers, and everyday people to compare cities based on what matters most to them, rather than relying on generic rankings that may not align with their specific needs and preferences.
For students and researchers, this tool demonstrates practical applications of weighted averages, normalization techniques, and composite scoring systems. The quality-of-life calculation shows how multiple factors can be combined using weighted formulas to create meaningful metrics that reflect different priorities. Business professionals can use custom-weighted comparisons to evaluate relocation opportunities, understand how different cities align with their career and lifestyle priorities, and assess whether job offers in different cities provide comparable quality of life based on their personal values. The tool helps HR professionals and job seekers understand that quality of life is multidimensional—a city might excel in job opportunities but struggle with housing affordability, or offer great amenities but have challenging commutes.
For the common person considering a move or evaluating their current city, this tool answers fundamental questions: Which city offers the best quality of life for my priorities? How do cities compare when I weight different factors differently? What are the trade-offs between cities with different strengths? The tool personalizes results by allowing you to adjust weights for each dimension—if housing affordability is your top priority, you can weight it heavily; if you value climate comfort most, you can emphasize that dimension. Taxpayers and budget-conscious individuals can use custom-weighted scores to identify cities that offer the best balance of affordability and quality of life based on their financial situation and lifestyle preferences.
The City Quality-of-Life Composite Score goes beyond simple rankings to provide a flexible, personalized assessment tool. By allowing custom weights, the tool recognizes that quality of life means different things to different people—a young professional might prioritize job opportunities and amenities, while a retiree might prioritize climate comfort and safety. Whether you're comparing two specific cities, exploring how different priorities affect rankings, or understanding the trade-offs between cities with different dimension profiles, this tool serves as your comprehensive guide to evaluating quality of life based on your personal values and priorities.
Understanding the Basics
The Seven Dimensions of Quality of Life
The tool evaluates cities across seven dimensions, each scored 0-100 where higher is always better: Housing Affordability (measures housing costs relative to income, inverted so higher scores mean more affordable), Job Opportunity (evaluates job market strength, wages, and employment prospects), Commute Ease (assesses commute burden, with higher scores indicating lighter burden), Transit Access (measures public transportation availability and convenience), Climate Comfort (evaluates year-round weather comfort based on temperature extremes and humidity), Safety (reflects crime levels, inverted so higher scores mean lower crime), and Amenities (measures access to parks, culture, dining, entertainment, and recreational facilities). All dimensions are oriented so that higher scores indicate better quality of life, making the composite calculation straightforward and intuitive.
Custom Weighting System
The tool allows you to adjust weights for each dimension from 0 to 5, reflecting your personal priorities. Weights are normalized to sum to 1.0 (100%), ensuring the composite score stays on a 0-100 scale. For example, if you set housing to 3, jobs to 2, and all others to 1, housing will represent approximately 30% of the composite score, jobs will represent 20%, and each other dimension will represent about 10%. Setting a weight to 0 effectively excludes that dimension from the calculation. This customization allows you to create a quality-of-life index that matches your values—if you don't drive and rely on public transit, you might weight transit access heavily; if you work remotely, you might weight commute ease at 0.
Composite Score Calculation
The composite score is a weighted average of all seven dimension scores using your normalized weights. The formula multiplies each dimension score by its normalized weight and sums the results: Composite Score = (Housing Score × Housing Weight) + (Job Score × Job Weight) + (Commute Score × Commute Weight) + (Transit Score × Transit Weight) + (Climate Score × Climate Weight) + (Safety Score × Safety Weight) + (Amenities Score × Amenities Weight). The result is rounded to one decimal place and ranges from 0-100, where higher scores indicate better overall quality of life based on your weighted priorities. This weighted approach ensures that dimensions you care about most have greater influence on the final score.
Dimension Scores and Interpretation
Each dimension is scored independently on a 0-100 scale, where higher is always better. These scores are based on city-wide data and represent typical conditions. The tool identifies the strongest dimension (highest score) and weakest dimension (lowest score) for each city, helping you understand where cities excel and where they struggle. Two cities might have identical composite scores but very different dimension profiles—one might excel in housing and jobs but struggle with climate and safety, while another might have balanced scores across all dimensions. Examining dimension breakdowns helps you understand trade-offs and identify cities that align with your specific priorities.
Score Interpretation Ranges
Composite scores are interpreted as: Excellent (80-100), Good (60-79), Moderate (40-59), Below Average (20-39), and Low (0-19). These ranges provide context for understanding where cities fall on the quality-of-life spectrum based on your weighted priorities. However, small differences (less than 5 points) are generally not meaningful due to data limitations and approximations. Focus on larger gaps and examine dimension-level differences to understand what's driving score variations. A 60 vs. 62 score difference is essentially a tie; a 60 vs. 75 difference is significant and reflects meaningful differences in quality of life based on your priorities.
Why Custom Weights Matter
Different people have different priorities, and generic quality-of-life rankings may not reflect your specific needs. A city that ranks highly with equal weights might rank lower if you heavily weight housing affordability, while a city that ranks lower with equal weights might rank higher if you heavily weight job opportunities. Custom weights allow you to create personalized rankings that match your values, life stage, and circumstances. For example, a young professional might weight jobs and amenities heavily, while a retiree might weight climate comfort and safety heavily. This flexibility makes the tool more useful than static rankings that assume everyone has the same priorities.
Step-by-Step Guide: How to Use This Tool
Step 1: Enter Your Primary City
Start by entering the name of the city you're interested in evaluating. Type the city name in the "City" field and select the corresponding state from the dropdown menu. The tool includes data for approximately 85 major US cities. If your city isn't in the database, the tool will display default moderate estimates, but results will be less accurate for specific comparisons.
Step 2: Adjust Dimension Weights to Match Your Priorities
Use the weight sliders to adjust the importance of each dimension from 0 to 5. If housing affordability is your top priority, set housing weight to 5 and others lower. If you work remotely and don't commute, set commute ease weight to 0. If you value climate comfort most, set climate weight to 5. The weights are automatically normalized to sum to 1.0 (100%), so you can use any values and the tool will proportionally adjust them. Experiment with different weight combinations to see how they affect city rankings and identify which cities align best with your priorities.
Step 3: (Optional) Add a Comparison City
To compare two cities side-by-side, enter a second city name and state in the "Comparison City" fields. Both cities will be evaluated using the same weights, ensuring fair comparisons. The comparison view shows composite scores, dimension breakdowns, and highlights where each city excels or struggles. This helps you understand trade-offs—for example, one city might have a higher composite score but lower scores in dimensions you care about most.
Step 4: Review the Results
After clicking "Calculate" or submitting the form, the tool displays comprehensive results including key performance indicators (KPIs), visualizations, and detailed metrics. The KPI section shows the composite score, strongest dimension, and weakest dimension at a glance. Visualizations help you understand the data through charts and graphs comparing dimension scores. The detailed results section provides a complete breakdown of all dimension scores, normalized weights, and composite calculations for your selected city (and comparison city, if provided).
Step 5: Interpret the Comparison Summary and Takeaways
If you compared two cities, read the comparison summary which explains how the cities differ in overall composite scores and specific dimensions. The summary identifies which city has a higher composite score based on your weights and explains the key differences. For example, it might state that "San Francisco (72.5) scores 8.3 points higher than Detroit (64.2) based on your weighted priorities. San Francisco's strength is Job Opportunity, while Detroit's strength is Housing Affordability." The key takeaways section highlights important insights, such as the biggest dimension gaps between cities and which dimensions favor which city.
Step 6: Experiment with Different Weight Combinations
Try different weight combinations to see how they affect city rankings. For example, compare cities with equal weights (all dimensions weighted equally) versus housing-heavy weights (housing weighted 5, others weighted 1) versus job-heavy weights (jobs weighted 5, others weighted 1). This experimentation helps you understand how different priorities affect quality-of-life assessments and identify cities that consistently rank well across multiple weight scenarios versus cities that only rank well with specific priorities.
Formulas and Behind-the-Scenes Logic
Weight Normalization
Raw weights entered by users are normalized to sum to 1.0 (100%) to ensure the composite score stays on a 0-100 scale:
Weight Sum = Housing Weight + Job Weight + Commute Weight + Transit Weight + Climate Weight + Safety Weight + Amenities Weight
If Weight Sum = 0: Use equal weights (1/7 each)
Normalized Weight = Raw Weight / Weight Sum
This normalization ensures that regardless of the raw weight values you enter, the proportions are preserved and the composite score remains on a 0-100 scale. For example, if you enter weights of 3, 2, 1, 1, 1, 1, 1 (sum = 10), they become 0.3, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1 (sum = 1.0) after normalization.
Composite Score Calculation
The composite score is calculated as a weighted average of all seven dimension scores:
Composite Score = (Housing Score × Housing Weight) + (Job Score × Job Weight) + (Commute Score × Commute Weight) + (Transit Score × Transit Weight) + (Climate Score × Climate Weight) + (Safety Score × Safety Weight) + (Amenities Score × Amenities Weight)
Result = Round(Composite Score × 10) / 10
Each dimension score (0-100) is multiplied by its normalized weight (0-1), and the results are summed. The final composite score is rounded to one decimal place and ranges from 0-100, where higher scores indicate better overall quality of life based on your weighted priorities.
Strongest and Weakest Dimension Identification
The tool identifies the strongest dimension (highest raw score) and weakest dimension (lowest raw score) for each city:
Strongest Dimension = Dimension with maximum score
Weakest Dimension = Dimension with minimum score
These identifications help you understand where cities excel and where they struggle, independent of your weights. A city might have a high composite score due to strong performance in dimensions you weight heavily, but still have weaknesses in other dimensions that you might want to consider.
Worked Example: San Francisco vs. Detroit with Different Weights
Let's calculate the composite score for San Francisco, California, using sample data with equal weights:
San Francisco Dimension Scores:
- Housing Affordability: 18
- Job Opportunity: 95
- Commute Ease: 55
- Transit Access: 78
- Climate Comfort: 82
- Safety: 45
- Amenities: 92
With Equal Weights (1/7 each ≈ 0.143):
Composite = (18 × 0.143) + (95 × 0.143) + (55 × 0.143) + (78 × 0.143) + (82 × 0.143) + (45 × 0.143) + (92 × 0.143)
= 2.57 + 13.59 + 7.87 + 11.15 + 11.73 + 6.44 + 13.16
= 66.5
Strongest Dimension: Job Opportunity (95)
Weakest Dimension: Housing Affordability (18)
Now let's compare with Detroit, Michigan, using housing-heavy weights (housing = 5, others = 1):
Detroit Dimension Scores:
- Housing Affordability: 72
- Job Opportunity: 65
- Commute Ease: 55
- Transit Access: 38
- Climate Comfort: 45
- Safety: 32
- Amenities: 62
With Housing-Heavy Weights (housing = 5, others = 1):
Weight Sum = 5 + 1 + 1 + 1 + 1 + 1 + 1 = 11
Normalized Weights: Housing = 5/11 ≈ 0.455, Others = 1/11 ≈ 0.091 each
Composite = (72 × 0.455) + (65 × 0.091) + (55 × 0.091) + (38 × 0.091) + (45 × 0.091) + (32 × 0.091) + (62 × 0.091)
= 32.76 + 5.92 + 5.01 + 3.46 + 4.10 + 2.91 + 5.64
= 59.8
With Equal Weights:
Composite = (72 + 65 + 55 + 38 + 45 + 32 + 62) / 7 = 369 / 7 = 52.7
Detroit Composite: 59.8 (housing-heavy) vs. 52.7 (equal weights)
With equal weights, San Francisco (66.5) scores higher than Detroit (52.7). However, with housing-heavy weights, Detroit (59.8) closes the gap significantly because it excels in housing affordability (72 vs. San Francisco's 18). This example demonstrates how custom weights can dramatically change city rankings based on your priorities. If housing affordability is your top concern, Detroit might be a better choice despite San Francisco's strengths in jobs, climate, and amenities.
Practical Use Cases
Student Research Project: Quality-of-Life Index Methodology
A student studying urban planning needs to understand how different weighting schemes affect city quality-of-life rankings. They use the tool to compare San Francisco and Detroit with equal weights (all dimensions weighted equally) versus housing-heavy weights (housing weighted 5, others weighted 1) versus job-heavy weights (jobs weighted 5, others weighted 1). The tool reveals that San Francisco ranks higher with equal weights (66.5 vs. 52.7) and job-heavy weights (85.2 vs. 65.0), but Detroit ranks higher with housing-heavy weights (59.8 vs. 35.4). This analysis helps the student understand how weighting methodology affects rankings and supports their research on quality-of-life measurement approaches.
Professional Relocation: Evaluating Job Offers with Personal Priorities
A software engineer receives job offers in San Francisco, California, and Austin, Texas. They use the tool to compare quality of life, weighting dimensions based on their priorities: jobs (5), housing (3), climate (2), amenities (2), commute (1), transit (0), safety (1). San Francisco shows composite score 78.5 (excellent), while Austin shows 72.3 (good). However, the engineer realizes that San Francisco's high score is driven by jobs and amenities, while Austin offers better housing affordability. They adjust weights to housing (5), jobs (3), climate (2), and see that Austin (68.2) now ranks closer to San Francisco (65.8), helping them understand the trade-offs and make an informed decision based on their priorities.
Researcher: Studying Quality-of-Life Trade-offs Across Cities
A researcher studying urban quality of life uses the tool to analyze how different priority profiles affect city rankings. They compare multiple cities using three weight profiles: affordability-focused (housing 5, jobs 2, others 1), opportunity-focused (jobs 5, amenities 3, others 1), and lifestyle-focused (climate 5, amenities 4, safety 3, others 1). The tool reveals that cities rank differently under different profiles—affordable cities like Detroit rank higher with affordability-focused weights, while opportunity-rich cities like San Francisco rank higher with opportunity-focused weights. This analysis supports their academic work on quality-of-life measurement and helps them understand how different stakeholder groups might evaluate cities differently.
Common Person: Finding Cities That Match Personal Priorities
A person planning to retire wants to find cities with excellent climate comfort and safety, with less concern about jobs and commute. They use the tool with weights: climate (5), safety (4), amenities (3), housing (2), transit (1), jobs (0), commute (0). They compare several potential retirement destinations: San Diego (composite 85.2), Phoenix (composite 48.5), Denver (composite 72.8), and Miami (composite 62.3). The tool shows that San Diego ranks highest with their priorities due to excellent climate (92) and good safety (58), while Phoenix ranks lower due to poor climate comfort (45) despite good housing affordability. This analysis helps them prioritize cities that align with their retirement lifestyle priorities.
Tax Payer: Balancing Affordability and Quality of Life
A budget-conscious individual wants to find cities that offer good quality of life without breaking the bank. They use the tool with weights: housing (5), safety (3), climate (2), jobs (2), amenities (1), commute (1), transit (0). They compare several cities: Detroit (composite 58.2), Cleveland (composite 55.8), Buffalo (composite 52.3), and Pittsburgh (composite 61.5). The tool shows that Pittsburgh ranks highest with their priorities due to good housing affordability (68) and safety (52), while offering moderate scores in other dimensions. This analysis helps them identify cities that provide the best balance of affordability and quality of life based on their financial constraints and lifestyle preferences.
Remote Worker: Prioritizing Lifestyle Over Commute
A remote worker who doesn't commute wants to find cities with excellent climate, amenities, and housing affordability, with no concern about jobs or commute. They use the tool with weights: climate (5), amenities (4), housing (3), safety (2), transit (1), jobs (0), commute (0). They compare several cities: San Diego (composite 82.5), Denver (composite 68.2), Austin (composite 64.8), and Portland (composite 62.3). The tool shows that San Diego ranks highest with their priorities due to excellent climate (92) and amenities (82), while offering moderate housing affordability (30). This analysis helps them understand that despite higher housing costs, San Diego offers the best overall lifestyle for remote workers who prioritize climate and amenities.
Understanding Why Two Cities Have Similar Scores with Different Profiles
A user notices that San Francisco (composite 66.5 with equal weights) and Chicago (composite 64.2 with equal weights) have similar overall scores but wants to understand the differences. The tool reveals that San Francisco excels in jobs (95), amenities (92), and climate (82) but struggles with housing (18) and safety (45). Chicago has more balanced scores: housing (45), jobs (80), amenities (88), commute (48), transit (72), climate (48), safety (38). The user learns that similar composite scores can mask significant differences in dimension profiles. San Francisco offers world-class jobs and amenities but at the cost of housing affordability, while Chicago offers more balanced quality of life across all dimensions. This understanding helps them identify which city aligns better with their specific priorities.
Common Mistakes to Avoid
Using Equal Weights Without Considering Your Priorities
The default equal weights assume all dimensions are equally important, which may not reflect your priorities. If housing affordability is your top concern, you should weight it heavily. If you work remotely and don't commute, you should set commute weight to 0. Always adjust weights to match your personal priorities, life stage, and circumstances. Generic rankings with equal weights may not identify the best cities for your specific situation.
Focusing Only on Composite Score Without Examining Dimensions
The composite score is useful for quick comparisons, but it can mask important differences in dimension profiles. Two cities might have identical composite scores but differ significantly in specific areas—one might excel in jobs and amenities but struggle with housing and safety, while another has balanced scores across all dimensions. Always examine individual dimension scores to understand what contributes to the overall score and which factors matter most to you. A city with a high composite score but low scores in dimensions you care about might not be ideal.
Overinterpreting Small Score Differences
Small differences (less than 5 points) in composite scores are generally not meaningful due to data limitations, approximations, and the subjective nature of quality-of-life measurement. A 60 vs. 62 score difference is essentially a tie. Focus on larger gaps (10+ points) and examine dimension-level differences to understand what's driving score variations. Don't make decisions based on tiny differences that could be due to data rounding or measurement error.
Ignoring Neighborhood-Level Variation Within Cities
The tool provides city-wide averages, but actual conditions vary dramatically within cities based on neighborhood, location, and other factors. A city with high overall safety scores might have unsafe neighborhoods, while a city with low overall housing affordability might have affordable neighborhoods. Don't assume the entire city has uniform conditions. If you're considering a specific neighborhood, research local conditions separately and use the tool as a starting point for city-level comparisons, not neighborhood-level decisions.
Not Experimenting with Different Weight Combinations
Quality of life is multidimensional, and different weight combinations can reveal different insights. Don't just use one weight profile—experiment with different combinations to see how they affect city rankings. Try equal weights, housing-heavy weights, job-heavy weights, climate-heavy weights, and other combinations. This experimentation helps you understand how different priorities affect assessments and identify cities that consistently rank well across multiple scenarios versus cities that only rank well with specific priorities.
Assuming Data Is Real-Time or Perfectly Accurate
The data is compiled from various public sources and represents approximate city-wide averages that may be 1-2 years old. It is not updated in real-time and may not reflect recent changes in housing markets, job markets, crime rates, or other factors. Use the tool as a starting point for research, not as a precise, up-to-the-minute assessment. Always supplement with current data from local sources, recent news, and personal research when making important decisions.
Making Relocation Decisions Based Solely on Composite Scores
Quality-of-life scores are one data point among many factors to consider when choosing where to live. Don't make relocation decisions based solely on composite scores without considering specific job opportunities, family needs, personal preferences, neighborhood characteristics, school quality, healthcare access, and other factors that matter to you. Use composite scores as a starting point for research, then conduct deeper investigation into cities that interest you, visit in person if possible, and consider all relevant factors before making decisions.
Advanced Tips & Strategies
Create Multiple Weight Profiles for Different Life Scenarios
Create different weight profiles for different life scenarios: young professional (jobs 5, amenities 4, housing 2, others 1), family with children (safety 5, housing 4, jobs 3, amenities 2, others 1), retiree (climate 5, safety 4, amenities 3, housing 2, jobs 0, commute 0, transit 1), remote worker (climate 5, amenities 4, housing 3, safety 2, jobs 0, commute 0, transit 1). Compare cities using each profile to see how they rank under different scenarios and identify cities that work well for multiple life stages.
Identify Cities That Rank Well Across Multiple Weight Profiles
Cities that rank well across multiple weight profiles are likely to offer balanced quality of life that works for different priorities. Compare cities using several different weight combinations and identify which cities consistently rank in the top tier regardless of weight profile. These cities likely offer strong performance across multiple dimensions, making them good choices if your priorities might change over time or if you're unsure about your exact priorities.
Use Dimension Breakdowns to Understand Trade-offs
When comparing cities, examine dimension breakdowns to understand trade-offs. A city might have a high composite score but low scores in dimensions you care about most. For example, San Francisco has excellent jobs and amenities but poor housing affordability. If housing is your top priority, you might prefer a city with lower composite score but better housing scores. Use dimension breakdowns to identify cities that excel in your priority areas, even if their overall composite score is lower.
Set Irrelevant Dimensions to Zero Weight
If certain dimensions don't matter to you, set their weights to 0 to exclude them from the calculation. For example, if you work remotely and never commute, set commute weight to 0. If you don't use public transit, set transit weight to 0. If you're retired, set job weight to 0. This focuses the composite score on dimensions that actually matter to you and produces more relevant rankings for your specific situation.
Compare Cities Using the Same Weights for Fair Comparisons
When comparing two cities, always use the same weights for both to ensure fair comparisons. The tool automatically applies the same weights to both cities, but if you're doing multiple comparisons, make sure you're using consistent weights across all comparisons. Different weights will produce different rankings, so comparing cities with different weight profiles isn't meaningful. Use consistent weights to understand how cities compare based on your priorities.
Combine with Other City Insights Tools for Comprehensive Analysis
Use this tool in conjunction with other city insights tools like cost-of-living calculators, tax burden comparisons, rent-to-income pressure calculators, commute burden indices, and climate comfort indices. Quality-of-life scores provide one perspective, but combining multiple tools gives you a more comprehensive view. For example, a city might have high quality-of-life scores but also high tax burden or rent pressure. Use multiple tools to understand the full picture before making decisions.
Research Neighborhood-Level Conditions for Shortlisted Cities
After using the tool to identify cities with favorable composite scores, research neighborhood-level conditions within those cities. City-wide averages may not reflect conditions in specific neighborhoods you're considering. Use local real estate websites, neighborhood review sites, local news, and if possible, visit in person to understand actual conditions. A city with high overall safety scores might have unsafe neighborhoods, while a city with low overall housing affordability might have affordable neighborhoods. Neighborhood-level research helps you make more accurate assessments.
Sources & References
The data and methodologies used in this tool are informed by authoritative sources on urban livability and quality of life metrics:
- •U.S. Census Bureau - American Community Survey: census.gov/programs-surveys/acs - Demographic, economic, housing, and social data for US communities.
- •Bureau of Labor Statistics - Local Area Employment: bls.gov/lau - Employment statistics and job market data for metropolitan areas.
- •FBI - Crime Data Explorer: crime-data-explorer.fr.cloud.gov - Official crime statistics used for safety dimension calculations.
- •NOAA - Climate Data Online: ncei.noaa.gov/cdo-web - Historical climate and weather data for climate comfort calculations.
- •American Public Transportation Association: apta.com - Transit ridership and accessibility data for transit dimension scores.
For Educational Purposes Only - Not Professional Advice
This calculator provides estimates for informational and educational purposes only. It does not constitute travel, financial, legal, or professional advice. Results are based on the information you provide and general guidelines that may not account for your individual circumstances. Costs, fees, and regulations change frequently. Always consult with a qualified licensed moving company or relocation specialist for advice specific to your situation. Information should be verified with official FMCSA.gov sources.
Frequently Asked Questions
Common questions about quality-of-life composite scores, dimension weighting, data sources, and how to use this tool for relocation planning.
What does the Quality-of-Life Composite Score measure?
The composite score combines seven dimensions of city livability (housing affordability, job opportunity, commute ease, transit access, climate comfort, safety, and amenities) into a single 0-100 score. You can adjust the weights of each dimension to reflect your personal priorities. Higher scores indicate better quality of life based on your weighted preferences.
How does the weight system work?
Each dimension has a weight you can adjust from 0 to 5. These weights are normalized to sum to 1.0 (100%). For example, if you set housing to 2 and all others to 1, housing will represent about 25% of the composite score. Setting a weight to 0 effectively excludes that dimension from the calculation.
Why are all dimension scores oriented as 'higher is better'?
To simplify interpretation, all scores are oriented so higher is always better. For dimensions like housing cost and crime, the raw data is inverted. A high 'Housing Affordability' score means housing is more affordable. A high 'Safety' score means lower crime. This ensures the composite calculation is straightforward.
Where does the data come from?
The data is compiled from various public sources including census data, cost-of-living indices, crime statistics, transit assessments, and climate data. The data represents approximate city-wide averages and may be 1-2 years old. It is not updated in real-time.
Why might my city not be found?
The tool includes data for approximately 85 major US cities. Smaller cities, suburbs, and rural areas may not be in the dataset. If your city isn't found, default moderate estimates are used, which may not accurately reflect your area.
Can I use this to decide where to move?
This tool provides one data-driven perspective on city quality of life, but it should not be your sole decision factor. Neighborhood-level variation, personal circumstances, family needs, specific job opportunities, and many other factors matter. Use this as a starting point for research, not a final answer.
How do I compare two cities fairly?
Enter both cities and use the same weights for both. The composite scores will reflect your priorities applied consistently to both cities. Look at both the overall composite and the individual dimension breakdowns to understand where each city excels or struggles.
What's the difference between composite score and dimension scores?
Dimension scores (0-100) measure each aspect independently. The composite score combines all dimensions using your weights. Two cities might have identical composites but very different dimension profiles. Always examine the breakdown to understand what's driving the overall score.
Does this tool account for cost of living adjustments?
The Housing Affordability dimension partially captures cost considerations. However, this is a quality-of-life index, not a full financial planning tool. It doesn't factor in your specific income, savings, debt, or detailed budget categories like groceries or utilities.
How should I interpret small score differences?
Small differences (less than 5 points) are generally not meaningful due to data limitations and approximations. Focus on larger gaps and examine dimension-level differences. A 60 vs 62 score difference is essentially a tie; a 60 vs 75 difference is significant.
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