Site Selection with Weighted Criteria
Learn how to eliminate poor site choices, score candidates using weighted criteria, and justify geographic decisions when weights change.
What you'll do in this lesson
A voice-first session with the Crimsora tutor on Site Selection with Weighted Criteria, then targeted practice and FRQs — with the tutor adapting to where you get stuck.
What this lesson covers
Choosing where to build a factory, open a store, or locate a hospital isn't just about finding any location—it's about finding the right location. Geographers use a process called site selection with weighted criteria to make these decisions fairly and logically. You'll start by setting aside any location that fails a must-have requirement (like "must have road access"), then score the remaining candidates by how well they match your priorities. Since different decision-makers care about different things, you'll also learn what happens when a weight changes—and how that can flip which site wins. This lesson teaches you the reasoning process that real planners and businesses use every day.
What Is Site Selection and Why Do Weights Matter?
Site selection is the process of choosing among several possible locations based on geographic criteria. A criterion is simply a requirement or factor that matters to your decision. When you have multiple criteria, some are more important than others—and that's where weighting comes in. A weight is a number you assign to show how important each criterion is compared to the others.
Imagine your town wants to build a new community center. It needs to be near homes (criterion: proximity to population), it needs parking (criterion: available land), and it should avoid flood zones (criterion: low flood risk). But these matter differently. Maybe proximity to homes is worth twice as much as parking, and flood risk is non-negotiable. Weights let you turn that priority into math. Without weights, you're just guessing. With them, you can defend your choice with numbers and show why another town official should agree with you.
Imagine your town wants to build a new community center. It needs to be near homes (criterion: proximity to population), it needs parking (criterion: available land), and it should avoid flood zones (criterion: low flood risk). But these matter differently. Maybe proximity to homes is worth twice as much as parking, and flood risk is non-negotiable. Weights let you turn that priority into math. Without weights, you're just guessing. With them, you can defend your choice with numbers and show why another town official should agree with you.
Must-Haves versus Weighted Criteria
Before you do any scoring, you must identify must-haves—criteria that any location must meet or it's automatically ruled out. These are absolute disqualifiers. If a site fails a must-have, no high score on other criteria can save it.
For example, if you're building a factory and a must-have is "must be within city limits," then a perfect site 20 miles away is eliminated instantly, no matter how cheap the land is. Must-haves are typically things that are impossible or illegal to change: distance to water for cooling, access to sewer systems, or zoning laws.
Once you eliminate all sites that fail a must-have, the remaining sites compete. Now you apply your weighted criteria. These are preferences that do vary in score—a site can score 8 out of 10 on one and 5 out of 10 on another. You multiply each site's score by the criterion's weight, add them up, and the highest total wins. Must-haves create a hard cutoff. Weights create a ranking.
For example, if you're building a factory and a must-have is "must be within city limits," then a perfect site 20 miles away is eliminated instantly, no matter how cheap the land is. Must-haves are typically things that are impossible or illegal to change: distance to water for cooling, access to sewer systems, or zoning laws.
Once you eliminate all sites that fail a must-have, the remaining sites compete. Now you apply your weighted criteria. These are preferences that do vary in score—a site can score 8 out of 10 on one and 5 out of 10 on another. You multiply each site's score by the criterion's weight, add them up, and the highest total wins. Must-haves create a hard cutoff. Weights create a ranking.
How to Score and Weight Candidates
Here's the step-by-step process:
Step 1: List all candidate sites. Write them down clearly.
Step 2: Identify must-haves and apply them. Cross out any site that fails. If all sites fail a must-have, the project can't happen—or you need new sites.
Step 3: List your weighted criteria and assign weights. Weights are usually numbers that sum to 100, or relative weights like 1, 2, 3 (meaning 3 is three times as important as 1). For example: Proximity to roads (weight 40), Land cost (weight 30), Environmental quality (weight 30).
Step 4: Score each remaining site on each criterion. Use a consistent scale: 0–10, 0–100, or any scale that makes sense. Be consistent. A score of 8 on proximity means the same thing for every site.
Step 5: Calculate weighted scores. For each site and each criterion, multiply the site's score by the criterion's weight. Add all weighted scores for that site. That total is its overall score.
Step 6: Compare totals and choose the winner. The site with the highest total wins—and you can now explain exactly why using numbers.
A mistake students often make: assigning weights that don't reflect reality. If you say parking is weight 1 and location is weight 50, you're saying location is 50 times more important. Make sure your weights match your actual priorities.
Step 1: List all candidate sites. Write them down clearly.
Step 2: Identify must-haves and apply them. Cross out any site that fails. If all sites fail a must-have, the project can't happen—or you need new sites.
Step 3: List your weighted criteria and assign weights. Weights are usually numbers that sum to 100, or relative weights like 1, 2, 3 (meaning 3 is three times as important as 1). For example: Proximity to roads (weight 40), Land cost (weight 30), Environmental quality (weight 30).
Step 4: Score each remaining site on each criterion. Use a consistent scale: 0–10, 0–100, or any scale that makes sense. Be consistent. A score of 8 on proximity means the same thing for every site.
Step 5: Calculate weighted scores. For each site and each criterion, multiply the site's score by the criterion's weight. Add all weighted scores for that site. That total is its overall score.
Step 6: Compare totals and choose the winner. The site with the highest total wins—and you can now explain exactly why using numbers.
A mistake students often make: assigning weights that don't reflect reality. If you say parking is weight 1 and location is weight 50, you're saying location is 50 times more important. Make sure your weights match your actual priorities.
How Changes in Weights Can Flip the Winner
One of the most important geographic insights is that changing priorities changes outcomes. In the real world, decision-makers disagree. A developer might weight low cost heavily; an environmentalist might weight low environmental impact heavily. A geographic analysis isn't complete until you've asked: what if the weights were different?
Suppose you're comparing three sites for a shopping mall. Site A scores high on proximity to highways (critical for customers driving in). Site B scores high on walkability and transit access (critical for pedestrians and buses). With weights that favor highway access, Site A wins. But if the city changes its goals and now weights walkability and transit twice as heavily as highway access, Site B wins instead.
This isn't a mistake—it's a feature. It shows that geographic choice is value-dependent. Different communities have different priorities, and those priorities should drive location decisions. By testing different weight scenarios, you show that you understand how geography responds to human values. It's also how planners build community support: "If these are your priorities, this site wins. If your priorities shift, this other site becomes better."
Suppose you're comparing three sites for a shopping mall. Site A scores high on proximity to highways (critical for customers driving in). Site B scores high on walkability and transit access (critical for pedestrians and buses). With weights that favor highway access, Site A wins. But if the city changes its goals and now weights walkability and transit twice as heavily as highway access, Site B wins instead.
This isn't a mistake—it's a feature. It shows that geographic choice is value-dependent. Different communities have different priorities, and those priorities should drive location decisions. By testing different weight scenarios, you show that you understand how geography responds to human values. It's also how planners build community support: "If these are your priorities, this site wins. If your priorities shift, this other site becomes better."
Common Mistakes and Where Students Go Wrong
Mixing up must-haves and weighted criteria. A must-have should be something truly non-negotiable. If you list "near downtown" as a must-have, you're saying any site not near downtown is ruled out entirely. That's rarely true. Save must-haves for real constraints (zoning, access to utilities, legal distance from homes).
Forgetting to eliminate must-have failures first. Some students try to score a site that fails a must-have and hope high scores elsewhere will balance it out. That defeats the purpose of a must-have. Eliminate first, then score.
Using weights that don't add up sensibly. If you assign weights randomly, your results will be meaningless. Pause and ask: does weight 50 for criterion A really mean it's five times more important than weight 10 for criterion B? If not, adjust.
Not documenting your scoring logic. Why did you score Site X as a 7 on environmental quality and Site Y as a 5? Can you point to something geographic (wetlands nearby? air quality data?) that justifies that difference? Vague scoring kills your credibility.
Treating the highest score as automatically correct. The numbers are a tool to organize your thinking, not a magic answer. If the math says Site A wins but you know from local knowledge that Site A is in a dangerous area, go back and check your must-haves and scores.
Forgetting to eliminate must-have failures first. Some students try to score a site that fails a must-have and hope high scores elsewhere will balance it out. That defeats the purpose of a must-have. Eliminate first, then score.
Using weights that don't add up sensibly. If you assign weights randomly, your results will be meaningless. Pause and ask: does weight 50 for criterion A really mean it's five times more important than weight 10 for criterion B? If not, adjust.
Not documenting your scoring logic. Why did you score Site X as a 7 on environmental quality and Site Y as a 5? Can you point to something geographic (wetlands nearby? air quality data?) that justifies that difference? Vague scoring kills your credibility.
Treating the highest score as automatically correct. The numbers are a tool to organize your thinking, not a magic answer. If the math says Site A wins but you know from local knowledge that Site A is in a dangerous area, go back and check your must-haves and scores.
Key terms
- Site selection.
- The process of choosing among candidate locations based on geographic criteria and priorities.
- Criterion (plural: criteria).
- A requirement or factor that is relevant to a location decision, such as proximity to roads, cost, or environmental quality.
- Must-have.
- A criterion that is non-negotiable; any site failing a must-have is immediately eliminated from consideration regardless of scores on other criteria.
- Weight.
- A number assigned to a criterion that shows how important it is relative to other criteria; higher weight means the criterion counts more in the final score.
- Weighted score.
- The result of multiplying a site's score on a single criterion by that criterion's weight; all weighted scores for a site are summed to get the site's total score.
- Elimination.
- The first step of site selection, in which any candidate site that fails a must-have criterion is removed from further consideration.
- Ranking.
- The ordering of remaining candidate sites from highest to lowest total weighted score.
Worked example
A town is selecting a site for a new youth center. Three candidate sites are being considered. The must-haves are: (1) must be within city limits, and (2) must be accessible by public transit. The weighted criteria are: proximity to population (weight 40), available facilities nearby (weight 30), and parking availability (weight 30). Use the scores below to determine the winner and explain why one site was eliminated.
| Site | Within city? | Transit access? | Population (0–10) | Facilities (0–10) | Parking (0–10) |
|---|---|---|---|---|---|
| A | Yes | Yes | 8 | 6 | 7 |
| B | Yes | No | 9 | 8 | 9 |
| C | No | Yes | 7 | 7 | 6 |
Step 1: Apply must-haves.
Must-have 1: Within city limits? Site C fails this. Site C is eliminated immediately.
Must-have 2: Transit access? Site B fails this. Site B is eliminated immediately.
Remaining site: Site A only.
Step 2: Score remaining sites.
Only Site A moves forward. We calculate its weighted score:Step 3: Choose the winner.
Site A has a total score of 710. It is the only site that passed both must-haves.
Explanation of eliminations:
Site C is in a beautiful area with good transit, but it is outside city limits. Building a youth center outside city limits would create liability and zoning issues, so no amount of high scores on other criteria can override this.
Site B is very close to population and has excellent facilities and parking, but it lacks public transit access. Young people need reliable ways to get there without a car. This is non-negotiable.
Site A is the clear choice because it meets both requirements and scores 8 out of 10 on the most heavily weighted criterion (population proximity). The town can feel confident in this decision because two locations were ruled out by firm geographic and planning constraints.
Must-have 1: Within city limits? Site C fails this. Site C is eliminated immediately.
Must-have 2: Transit access? Site B fails this. Site B is eliminated immediately.
Remaining site: Site A only.
Step 2: Score remaining sites.
Only Site A moves forward. We calculate its weighted score:Step 3: Choose the winner.
Site A has a total score of 710. It is the only site that passed both must-haves.
Explanation of eliminations:
Site C is in a beautiful area with good transit, but it is outside city limits. Building a youth center outside city limits would create liability and zoning issues, so no amount of high scores on other criteria can override this.
Site B is very close to population and has excellent facilities and parking, but it lacks public transit access. Young people need reliable ways to get there without a car. This is non-negotiable.
Site A is the clear choice because it meets both requirements and scores 8 out of 10 on the most heavily weighted criterion (population proximity). The town can feel confident in this decision because two locations were ruled out by firm geographic and planning constraints.
Practice questions
Three sites (P, Q, R) are scored on land cost and location quality. Site P scores 6 on land cost (weight 50) and 9 on location quality (weight 50). Site Q scores 8 on land cost (weight 50) and 7 on location quality (weight 50). Which site has the higher total weighted score?
- Site P (total 7.5)
- Site Q (total 7.5)
- Site P (total 750)
- Site Q (total 750)
Answer: Site Q (total 750)
For Site P: . For Site Q: . Both sites actually tie at 750. The question tests whether you can calculate weighted scores correctly. A common error is to average the scores (which gives 7.5) instead of weighting them. Another is forgetting to multiply by the weight at all. Here, both sites score the same total, so in a real decision, you'd look at other factors or ask if any score was calculated incorrectly.
A city is choosing between two warehouse sites. The must-haves are: (1) within 5 miles of a highway, and (2) available immediately. Site X is 3 miles from a highway and available in 6 months. Site Y is 6 miles from a highway and available now. Using only these must-haves, which site(s) should advance to the weighted scoring stage? Explain your reasoning.
Answer: Only Site Y should advance. Site X fails must-have 1 (it is 3 miles from a highway, but the requirement is within 5 miles—wait, that is within 5 miles, so Site X passes must-have 1). Site X fails must-have 2 (it is not available immediately). Site Y passes must-have 1 (6 miles is not within 5 miles, so Site Y actually fails must-have 1). Let me recalculate: Site X is 3 miles away, which IS within 5 miles—it passes. Site X is not available immediately, so it fails must-have 2. Site Y is 6 miles away, which is NOT within 5 miles—it fails must-have 1. Both sites fail at least one must-have, so neither advances.
This question tests your understanding of must-haves as absolute cutoffs. The correct answer is that neither site advances—both fail at least one must-have. Site X is close enough to the highway but too slow to acquire. Site Y is available now but too far from the highway. Neither can be scored because neither meets all must-haves. This is a realistic scenario: sometimes a decision cannot be made with the sites available, and the search must continue. A student error would be to score both sites anyway and hope one 'wins,' but that violates the logic of must-haves. Another error is misreading the distance: '6 miles' is not within '5 miles,' so Site Y fails that criterion.
You are analyzing a site selection for a new restaurant. Criterion A (location visibility) has weight 30, and Criterion B (parking) has weight 20. You realize the developer actually cares much more about parking in this area. How would you adjust the weights to reflect this new priority, and what effect would that change have on the overall site selection?
Answer: Increase the weight for parking (Criterion B) and decrease the weight for visibility (Criterion A). For example, change to visibility weight 15 and parking weight 35. Or adjust so parking weight is double or triple visibility weight. The effect: A site with high parking scores would now earn a much higher overall score, potentially changing which site wins. A site that was losing because of low parking scores might now win if parking is weighted heavily enough.
This question teaches the key insight that weights are not fixed facts—they reflect human values and priorities. By lowering visibility from 30 to 15 and raising parking from 20 to 35, you're telling the story of the restaurant owner's priorities. The site that excels at parking will now benefit much more in the final calculation. If you run the weighted scores again with the new weights, you may find that the original winning site (high visibility, mediocre parking) now loses to a site with lower visibility but excellent parking. This is not a flaw in the method; it's the method working correctly. It shows that site selection is transparent and value-dependent. Different priorities lead to different outcomes.
FAQ
- What's the difference between a must-have and a weighted criterion?
- A must-have is absolute: if a site fails it, the site is eliminated immediately and does not get scored. A weighted criterion is a preference that varies in strength: sites can score differently on it, and those differences affect the final ranking. For example, 'must have road access within 1 mile' is a must-have; if a site has no road access, it's out. But 'proximity to roads is preferred' with weight 40 is a weighted criterion: a site with a road 100 meters away scores higher than one with a road 800 meters away, but both can compete.
- Can I use decimal weights, or do they have to be whole numbers?
- You can use decimals or whole numbers. Weights are just relative—what matters is the ratio between them. If you use weights 1.5 and 3, that's the same as 1 and 2 or 10 and 20: criterion B is twice as important as criterion A. Some people prefer whole numbers because they're easier to calculate by hand. Others use decimal weights that sum to 1.0 or percentages that sum to 100. Pick whichever makes sense to you and stick with it.
- What if two sites tie with the same total weighted score?
- That's real. When sites tie, you have a few options: (1) re-examine your scoring—did you really mean to score them identically on every criterion, or did you round too much?; (2) look at secondary criteria you didn't weight heavily, like local preference or past experience; (3) ask the decision-maker if there's a hidden priority that should have been weighted; or (4) acknowledge that both sites are equally good and either one is defensible. Ties are not a failure. They show that your analysis is honest and that the two locations are genuinely similar in quality.
- Can weights change during the process, or should they be set before I start scoring?
- Set your weights before you score. If you see the results and then change weights to make a preferred site win, you're cheating your own analysis. That said, in real-world planning, stakeholders often debate what the weights should be before any site is evaluated. It's perfectly normal to say, 'If we weight environmental quality at 50, Site A wins. If we weight it at 20, Site C wins. Which priority do you want?' That's educational. But once you've decided on weights together, lock them in and calculate fairly.
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The Crimsora tutor teaches Site Selection with Weighted Criteria live — explaining on a whiteboard, asking you questions, and adapting to where you get stuck.