Location, Site and Geospatial Analysis

Introduction and Context

Real estate value is inseparable from place. A building may be physically sound and financially well designed, yet fail commercially because it is difficult to reach, poorly visible, exposed to environmental hazards, or inconsistent with planning controls. Location analysis therefore examines not merely where a property is situated, but how its position interacts with markets, movement systems, infrastructure, regulation, environmental conditions and competing land uses.

In real estate market research, location analysis supports acquisition, feasibility, development appraisal, asset management, investment underwriting and public-sector planning. It helps answer questions such as:

• Who can reach the site, and how easily?
• What population, employment and spending potential lies within its market area?
• How does the site compare with competing locations?
• What infrastructure and amenities support or constrain value?
• What planning, environmental or legal restrictions affect development?
• How should spatial evidence be integrated into a location decision?

A rigorous analysis combines field observation, secondary data, spatial databases, geographic information systems (GIS), network analysis, statistical methods and professional judgment. No single map or score is sufficient. The analyst must establish a clear decision purpose, use appropriate spatial units, test assumptions and communicate uncertainty.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Distinguish location, site, situation, accessibility, visibility and amenity effects in real estate analysis.
2. Define trade areas and catchment areas using defensible demographic, behavioral and network-based evidence.
3. Assess transport access, infrastructure, amenities, environmental constraints and planning controls.
4. Apply GIS, spatial statistics, network analysis and gravity models to real estate questions.
5. Construct and critically evaluate a location-scoring model.
6. Recognize spatial-data limitations, ethical risks and governance requirements.
7. Translate geospatial findings into investment, development and asset-management decisions.

1. Location as a Real Estate Variable

1.1 Site, location and situation

A site is the specific parcel or development plot, including its boundaries, physical characteristics, access points, services and legal attributes. Location refers to the site’s position relative to people, activities, transport, infrastructure, competitors and institutions. Situation is the broader regional and urban context within which the site operates.

A useful distinction is:

• Site factors: area, shape, slope, soil, frontage, easements, utilities and development rights.
• Locational factors: proximity to employment, retail, schools, hospitals, transit and competitors.
• Situational factors: metropolitan growth, regional connectivity, economic structure, governance and long-term urban change.

Location is multidimensional. A central site may have high accessibility but expensive land and severe congestion. A peripheral site may offer larger plots and lower costs but depend on private vehicles. A visible retail site may be unsuitable for housing because of noise and traffic. Thus, the relevant location is determined by the use being analyzed.

1.2 Accessibility, mobility and visibility

Accessibility is the ease with which people, goods or services can reach a site or reach destinations from it. It differs from simple distance. A site 3 km away may be less accessible than a site 8 km away if the nearer route is congested or lacks a crossing.

Accessibility may be measured by:

• travel time;
• generalized travel cost, including fares, fuel, tolls and time;
• number of destinations reachable within a time threshold;
• frequency and reliability of public transport;
• pedestrian and cycling connectivity;
• freight access and loading conditions.

Visibility concerns the probability that a site or its signage will be noticed by passing or nearby users. It depends on traffic volume, pedestrian flows, sight lines, topography, building orientation, setbacks, obstructions and sign regulations. Visibility is especially important for convenience retail, restaurants, automotive services and hospitality, but less decisive for destination offices or industrial premises.

1.3 Agglomeration and externalities

Real estate demand is affected by positive and negative externalities. Agglomeration economies arise when firms and users benefit from proximity to one another through shared labor markets, suppliers, information, customers and infrastructure. Retail clusters may attract comparison shopping, while office districts may support specialized services.

Negative externalities include noise, pollution, congestion, crime perception, flood risk, incompatible industrial activity and construction disruption. The analyst should identify both the benefits and costs of proximity rather than assuming that “more central” always means “better.”

2. Site and Context Investigation

A professional site investigation proceeds from regional context to parcel detail.

2.1 Regional and submarket context

The first stage examines population and household growth, employment concentrations, income, housing supply, major infrastructure, development pipelines and policy priorities. A site should be interpreted within its submarket rather than evaluated in isolation. For example, a new transit station may improve accessibility, but the effect depends on whether surrounding development is permitted and whether competing stations serve the same population.

2.2 Parcel-level characteristics

Parcel analysis should document:

• legal boundaries and title restrictions;
• dimensions, frontage and depth;
• slope, elevation and drainage;
• soil and geotechnical conditions;
• existing structures and demolition requirements;
• rights of way, covenants and easements;
• utility capacity and connection costs;
• access for vehicles, pedestrians, emergency services and deliveries;
• adjoining uses and potential nuisance conditions.

A cadastral map is not a substitute for a legal survey. GIS parcel boundaries may be generalized, outdated or misaligned with title records. Critical decisions require verified survey, title and engineering information.

2.3 Infrastructure and public services

Infrastructure analysis should distinguish availability from capacity. A water main may be adjacent to a site, but insufficient pressure or treatment capacity may require costly upgrading. Relevant systems include roads, public transport, water, wastewater, stormwater, electricity, telecommunications, solid waste and emergency services.

The analyst should record the provider, connection point, spare capacity, upgrade obligation, timing and estimated cost. Infrastructure risk is often a development-timing risk: a project may be economically feasible only if a planned road or station is delivered as scheduled.

2.4 Amenities and disamenities

Amenities may be classified as:

Distance alone is inadequate. An amenity’s quality, capacity, opening hours, safety and actual use matter. A small park may be more valuable than a larger but inaccessible park. Amenity effects may also be nonlinear: the first nearby supermarket may matter greatly, while additional supermarkets add little value.

3. Trade Areas and Catchment Areas

A trade area is the geographic zone from which a commercial property draws customers. A catchment area is a broader term describing the population or territory served by a facility, such as a school, hospital or retail center. A primary catchment supplies the largest share of users, while secondary and tertiary catchments contribute progressively smaller shares.

3.1 Methods of delineation

#### Fixed-radius method

A radius around a site is simple and transparent. It may be expressed in kilometers or travel time. However, circular areas ignore rivers, railways, restricted crossings, topography and transport differences. A 5 km radius is therefore a screening device, not necessarily a realistic market boundary.

#### Drive-time and walk-time method

Network-based catchments calculate areas reachable within specified travel times. They are preferable when road configuration and congestion materially affect behavior. Separate catchments should normally be prepared for driving, walking, cycling and public transport.

#### Customer-origin method

Existing customer addresses, loyalty-card records, delivery records or anonymized mobile data can reveal the actual origin of demand. These methods are behaviorally stronger but may suffer from sample bias, privacy restrictions and incomplete coverage.

#### Competitive and behavioral method

A trade area may be bounded by major competitors, physical barriers, shopping habits, destination attractiveness and brand loyalty. For a dominant regional mall, the catchment may extend far beyond the area of a neighborhood center.

3.2 Catchment estimation

If population counts are available by spatial unit, catchment population can be estimated by overlaying the catchment polygon and aggregating intersecting units. When only partial units fall inside the boundary, proportional allocation may be used cautiously:

where P_c is estimated catchment population, Pᵢ is the population of spatial unit i, and wᵢ is the proportion of unit i assigned to the catchment. The method assumes population is distributed uniformly within each unit, an assumption that can be poor in heterogeneous neighborhoods.

A stronger method uses address points, building footprints or gridded population data. Analysts should avoid double counting overlapping catchments and should distinguish resident population from daytime population, tourist flows, students and workers.

4. GIS and Spatial Data Architecture

GIS links geographic features to attributes. A real estate GIS may include parcels, buildings, roads, transit stops, administrative boundaries, demographics, flood zones, planning designations, environmental sites, transactions and competing properties.

4.1 Vector and raster data

Vector data represent points, lines and polygons. Examples include schools, roads, parcels and zoning districts. Raster data represent a continuous surface or grid, such as elevation, satellite imagery, flood depth or population density.

The analyst must check:

• coordinate reference system;
• positional accuracy;
• date and update frequency;
• scale and resolution;
• completeness and missing values;
• licensing and permitted use;
• definitions and metadata.

Spatial overlays can identify parcels intersecting constraints or lying within specified distances. Buffers are useful but can create false precision when the underlying data are coarse. All layers should be projected appropriately before distance or area calculations; geographic degrees are not suitable units for most local metric calculations.

4.2 Geocoding and data quality

Geocoding converts addresses into coordinates. Errors arise from incomplete addresses, multiple buildings, informal settlements and changes in street names. A quality-control process should inspect unmatched records, positional confidence and duplicate locations.

Spatial joins can incorrectly assign data near boundaries. For example, a population value attached to a census tract does not mean that all residents live near the site. Analysts should disclose whether results are based on centroids, areal interpolation, address points or gridded estimates.

5. Network Analysis and Accessibility Metrics

Network analysis represents movement as nodes and links. Nodes may be intersections, stations or facilities; links represent roads, paths or transit segments with attributes such as length, speed, capacity, slope and travel time.

5.1 Shortest-path and service-area analysis

The shortest path minimizes a specified impedance, often travel time rather than geometric distance. A service area identifies destinations reachable within thresholds such as 10, 20 or 30 minutes. Travel times should reflect mode, time of day, direction, transfers and, where possible, reliability.

For link i, travel time may be represented as:

where tᵢ is travel time, dᵢ is link distance, vᵢ is average speed, and hᵢ represents additional delay such as intersections, boarding or transfer time. Units must be consistent; for example, distance in kilometers and speed in kilometers per hour produce time in hours. The formula is a simplified representation and should not replace calibrated traffic or transit models where congestion is significant.

5.2 Accessibility indices

A cumulative-opportunity measure counts opportunities reachable within a threshold:

where A(T) is accessibility within time threshold T, Oⱼ is the amount of opportunity at destination j, tᵢⱼ is travel time from origin i to destination j, and I(·) equals 1 when the condition is true and 0 otherwise. Opportunities may be jobs, households, schools or retail floor area.

A gravity-based accessibility measure gives decreasing weight to more distant destinations:

where cᵢⱼ is generalized travel cost and f(cᵢⱼ) is a non-negative distance-decay function. Common choices include f(c)=e^-βc or f(c)=c^-α, with positive parameters α or β estimated or justified from observed behavior. The function must match the context and should not be selected merely because it produces a desirable ranking.

6. Gravity Models and Spatial Interaction

Gravity models estimate flows between origins and destinations. A general form is:

where Fᵢⱼ is the predicted flow from origin i to destination j, Aᵢ is origin mass such as population or purchasing power, Bⱼ is destination attractiveness such as floor area or sales capacity, K is a scaling constant, α and β are estimated elasticities, and f is a distance-decay function.

A retail application may use households as origin mass and store size, brand strength or tenant mix as destination attractiveness. Gravity models are useful for scenario comparison, but they are not causal proof. They may omit price, quality, competition, transport mode, psychological preferences and capacity constraints. Calibration should use observed customer flows where possible, and results should be tested through sensitivity analysis.

A related probabilistic formulation is the Huff model:

where Pᵢⱼ is the probability that consumers from origin i choose destination j, Sⱼ is destination attractiveness, cᵢⱼ is travel cost, and α and β are parameters. The denominator includes all relevant competing destinations k. The model assumes that the specified destinations adequately represent consumer choice and that parameters are appropriate for the market.

7. Spatial Statistics and Market Evidence

Spatial data often violate the assumption that observations are independent. Nearby property prices may resemble one another because they share schools, infrastructure, zoning and neighborhood reputation. This phenomenon is spatial autocorrelation.

A commonly used global measure is Moran’s I:

Here n is the number of observations, xᵢ is the value at location i, x̄ is the mean, wᵢⱼ is the spatial-weight between i and j, and W is the sum of all weights. Positive values generally indicate clustering of similar values; negative values indicate neighboring dissimilarity. Interpretation depends on the weight matrix, sample design and significance testing.

Spatial regression may be used when prices or rents are related to both property characteristics and location. A simple spatial-lag model is:

where y is the dependent variable, W is a spatial-weights matrix, ρ is the spatial-lag parameter, X is a matrix of explanatory variables, β is a coefficient vector and ε is an error term. The model assumes an appropriate specification, valid weights and sufficiently reliable data. It should not be interpreted as automatically resolving omitted-variable bias or causality.

For real estate, spatial statistics can help identify price clusters, underserved areas, residual land-value patterns and anomalous transactions. Analysts must guard against the modifiable areal unit problem: results may change when the same data are grouped into different spatial boundaries.

8. Environmental Constraints and Planning Controls

Location analysis must treat environmental and regulatory conditions as development determinants, not as peripheral annotations.

8.1 Environmental constraints

Relevant factors include floodplains, coastal inundation, erosion, wildfire, landslide susceptibility, contaminated land, wetlands, protected habitats, air quality, noise, heat exposure and water scarcity. Analysis should distinguish hazard probability, hazard intensity, exposure and vulnerability.

A site outside a mapped flood zone is not necessarily risk-free. Mapping may be outdated, local drainage may be inadequate and future climate conditions may differ from historical assumptions. Due diligence should identify the responsible authority, map date, hazard standard, required studies, mitigation measures and residual risk.

8.2 Planning and development controls

Planning analysis should review:

• permitted and conditional uses;
• zoning and overlay districts;
• floor-area ratio or plot ratio;
• site coverage and setbacks;
• building height and massing;
• density and unit restrictions;
• parking and loading standards;
• heritage and design controls;
• affordable-housing or inclusionary obligations;
• environmental-impact requirements;
• subdivision, access and infrastructure conditions.

Planning controls are not simply constraints. They can create value by limiting competing supply, enabling higher density or supporting a preferred land use. Conversely, a favorable zoning designation may have limited economic value if approvals are uncertain or infrastructure is unavailable.

9. Location Scoring and Multi-Criteria Decision Analysis

A location score organizes diverse evidence into a transparent decision framework. A basic weighted-sum model is:

where Sᵢ is the score of site i, wₖ is the weight of criterion k, and zᵢₖ is the normalized performance of site i on criterion k. Usually, weights are non-negative and sum to 1. Criteria must be oriented so that higher values consistently mean better performance.

For a benefit criterion, min–max normalization may be written:

For a cost criterion, the direction is reversed:

These formulas require a nonzero range and comparable observations. Outliers can dominate the range, so percentile scaling or theoretically meaningful thresholds may be preferable. A score is a decision aid, not an objective truth. Weights reflect values and assumptions; they should be agreed before reviewing outcomes where possible.

A practical framework may use the following groups:

Sensitivity analysis should vary weights, thresholds and uncertain data. If a site ranks first only under one narrow weighting scheme, the conclusion should be presented as fragile.

10. Worked Example: Comparing Two Illustrative Sites

Suppose an investor is comparing Sites A and B for a neighborhood supermarket. Four criteria are selected: catchment households, drive-time accessibility, visibility and development risk. The first three are benefits; risk is a cost. Weights are 0.35, 0.25, 0.20 and 0.20 respectively.

Raw data are:

Using min–max normalization, Site A’s catchment score is 1 and Site B’s is 0. Site A’s 15-minute score is 0 and Site B’s is 1. Site A’s visibility score is 0 and Site B’s is 1. For risk, the lower value is better, so Site A receives 1 and Site B receives 0.

Therefore:

Site A ranks higher under these assumptions because its stronger local market and lower risk outweigh Site B’s accessibility and visibility. However, this is not a final investment conclusion. The analyst should test whether the risk index is reliable, whether Site B’s larger 15-minute market is genuinely relevant to supermarket trips, and whether Site A can support adequate parking and delivery access.

11. Illustrative Case: Transit-Oriented Mixed-Use Development

Background

A development company is considering a mixed-use project near a proposed metropolitan rail station. The concept includes apartments, small-format retail and flexible office space. The station is expected to open in four years, but construction funding has not yet been finalized.

Problem

The site appears attractive because it is close to the planned station and a university. However, it is also exposed to flood risk, lacks a direct pedestrian crossing over a six-lane road and faces a competing development with an earlier approval.

Analysis

The research team constructs separate walking, cycling, driving and public-transport networks. A 10-minute pedestrian catchment initially appears large, but the six-lane road reduces effective access to the station. A network service area incorporating safe crossings reduces the practical catchment substantially.

The team overlays parcels with zoning, flood maps, utility networks and heritage controls. The site permits high-density mixed use, but a flood-resilient design, elevated plant and stormwater detention are required. Utility records show that wastewater capacity is adequate only after a planned upgrade.

A gravity-based analysis estimates university-related retail demand under three scenarios: station delivered on time, station delayed two years and station cancelled. Under the delay scenario, office absorption falls and car-parking demand increases. Under cancellation, the project remains feasible only at lower density and with a greater residential component.

Approach and solution

The company adopts a staged strategy:

1. Secure planning approval that preserves flexibility among office, residential and retail uses.
2. Obtain contractual commitments concerning the wastewater upgrade.
3. Design a safe pedestrian crossing with the transport authority.
4. Use flood-resilient construction and commission a detailed hydrological study.
5. Phase construction so that residential units can proceed without depending entirely on station-related office demand.
6. Include explicit station-delay and cancellation scenarios in the financial model.

Outcome

The project is not rejected, but the land offer is reduced to reflect infrastructure and transit uncertainty. The final design allocates less space to speculative office use and more to adaptable residential and neighborhood retail space. Investment approval is made conditional on infrastructure milestones.

Learning points

The case illustrates that proximity is not the same as accessibility; planned infrastructure is not the same as delivered infrastructure; and a location score should incorporate timing, uncertainty and alternative scenarios. It also demonstrates the value of combining GIS evidence with legal, engineering, planning and financial analysis.

12. Professional Practice, Ethics and Governance

Geospatial analysis can influence land prices, displacement, public investment and access to services. Analysts should therefore address ethical and governance issues.

First, personal mobility, customer and device data may identify individuals or sensitive groups. Data should be anonymized, aggregated and processed under applicable privacy law. Collection should be proportionate to the decision purpose.

Second, models can reproduce bias. Areas with poor data may appear less attractive, while historic lending, policing or investment patterns may encode discrimination. Analysts should test whether indicators act as proxies for protected characteristics and should avoid using sensitive data without a legitimate, transparent basis.

Third, maps can create false authority. A visually precise map may conceal outdated boundaries, uncertain flood estimates or incomplete infrastructure records. Every professional report should state data dates, sources, scale, uncertainty and limitations.

Fourth, planning and environmental information may be legally consequential. Analysts should distinguish preliminary research from certified survey, environmental assessment, title advice or planning counsel. Critical conclusions require verification by qualified specialists.

13. Technology and Contemporary Developments

Remote sensing, cloud GIS, three-dimensional city models, open data portals, digital twins and machine learning are expanding spatial analysis. Satellite imagery can monitor land-use change; street-level imagery can support visibility and frontage assessment; and anonymized mobility data can improve travel-pattern analysis.

AI can assist with geocoding, document extraction, image classification, scenario generation and anomaly detection. However, AI outputs require human validation. Models may inherit spatial bias, misclassify land uses, hallucinate planning rules or infer behavior from unrepresentative data. A sound workflow records the source, model version, validation method and human review.

Digital twins may connect buildings, infrastructure, mobility and environmental systems for scenario testing. Their value depends on data interoperability, update frequency and institutional governance. Sophisticated technology does not eliminate the need for field inspection, stakeholder consultation and professional judgment.

Best-Practice Workflow

A defensible location study should follow these steps:

1. Define the decision, asset type, users, time horizon and success criteria.
2. Establish the study area and relevant market or catchment concepts.
3. Assemble authoritative spatial, demographic, market, infrastructure and regulatory data.
4. Verify coordinate systems, dates, definitions, quality and legal permissions.
5. Inspect the site physically and document access, visibility, nuisances and context.
6. Build mode-specific networks and calculate realistic travel times.
7. Map competition, amenities, constraints and development opportunities.
8. Apply appropriate statistical or gravity models, explaining assumptions.
9. Normalize and weight location criteria transparently if a score is used.
10. Test sensitivity, uncertainty, alternative scenarios and potential bias.
11. Validate important results with planners, engineers, brokers, transport specialists and local stakeholders.
12. Translate findings into acquisition conditions, design responses, pricing, phasing and risk controls.

Challenges and Limitations

Location analysis faces several recurring limitations. Data may be incomplete, inconsistent or unavailable at the necessary scale. Travel behavior changes with fuel prices, remote work, service quality and social conditions. Planned infrastructure may be delayed. Catchment boundaries are behavioral rather than fixed. Spatial averages can conceal inequality within neighborhoods. Correlation between proximity and value does not establish causation. Finally, a highly ranked site may still fail because of land price, construction cost, financing or execution risk.

The correct response is not to abandon quantitative analysis, but to combine it with triangulation, scenario analysis, field evidence and explicit disclosure. Good geospatial research makes uncertainty visible and decision-relevant.

Chapter Summary

Location, site and geospatial analysis connect physical place with real estate demand, value and development feasibility. Site characteristics include parcel geometry, access, utilities and environmental conditions; location characteristics include accessibility, visibility, amenities, competition and proximity to economic activity; and situation concerns the wider regional and policy context.

Trade and catchment areas should be defined according to asset type and user behavior. Network-based travel times are generally more realistic than simple radii, while customer-origin evidence can validate modeled areas. GIS supports overlays, buffers, service areas, market mapping and scenario analysis, but results depend on data quality, scale and coordinate integrity.

Accessibility indices and gravity models estimate opportunities and potential flows, while spatial statistics help identify clustering and account for spatial dependence. Planning controls and environmental hazards must be integrated into feasibility analysis. Weighted location scores provide transparency, but their results depend on normalization, weights and assumptions; sensitivity analysis is essential.

Professional practice requires privacy protection, bias assessment, source documentation, specialist verification and clear communication of uncertainty. The strongest location studies combine spatial technology with market knowledge, field observation, planning interpretation, engineering review and financial scenario analysis.

Key Terms

Location: The position of a property relative to markets, infrastructure, activities and competing uses.

Site: The specific parcel or development plot and its physical and legal attributes.

Situation: The wider regional, metropolitan and policy context of a property.

Accessibility: The ease of reaching destinations or being reached by users, measured through time, cost or opportunities.

Visibility: The likelihood that a property, frontage or sign will be noticed.

Trade area: The geographic area from which a commercial property attracts customers.

Catchment area: The population or territory served by a facility or property.

Primary catchment: The area supplying the largest proportion of users or customers.

Geographic information system (GIS): A system for storing, analyzing and displaying geographically referenced data.

Vector data: Spatial data represented as points, lines or polygons.

Raster data: Spatial data represented as a grid of cells or pixels.

Network analysis: Analysis of movement through connected nodes and links.

Service area: The area reachable from a location within a specified travel-time or cost threshold.

Gravity model: A spatial-interaction model in which flows depend on origin mass, destination attractiveness and separation.

Spatial autocorrelation: The degree to which nearby observations have similar or dissimilar values.

Spatial regression: Regression that accounts for spatial dependence among observations.

Location scoring: The systematic ranking of sites using weighted criteria.

Environmental constraint: A hazard, ecological condition or regulatory requirement that limits or affects development.

Modifiable areal unit problem: The possibility that statistical results change when spatial observations are grouped differently.

Digital twin: A data-connected digital representation of a physical asset, place or system.

Review Questions

Short Answer Questions

1. Distinguish between a site, a location and a situation in real estate analysis.
2. Why can a network-based catchment be more reliable than a circular-radius catchment?
3. Identify four types of infrastructure that should be examined in a site study.
4. What is the purpose of normalizing criteria in a location-scoring model?
5. State two ethical risks associated with using mobility or customer-location data.

Long Answer Questions

1. Explain how accessibility, visibility, amenities and externalities influence different real estate uses.
2. Describe a complete GIS workflow for evaluating a proposed mixed-use development.
3. Compare fixed-radius, drive-time, customer-origin and behavioral methods of defining trade areas.
4. Explain the assumptions and limitations of gravity models and Huff-type retail models.
5. Discuss how planning controls and environmental constraints should be incorporated into development feasibility analysis.

Analytical / Application Questions

1. Design a catchment methodology for a new neighborhood shopping center. Specify data sources, travel modes, time thresholds, competition treatment and validation procedures.
2. Construct a weighted location-scoring framework for comparing two logistics sites. Justify the criteria, weights, normalization method and sensitivity tests.
3. A proposed transit station is scheduled to open in five years but has uncertain funding. Develop a spatial and investment-analysis approach that incorporates delay, cancellation, accessibility, infrastructure and alternative-use scenarios.

Multiple Choice Questions with Answers

Question 1

Which characteristic most directly distinguishes accessibility from simple distance?

A. Accessibility considers travel time, cost and network conditions

B. Accessibility is always measured as straight-line distance

C. Accessibility applies only to pedestrian movement

D. Accessibility excludes public transport conditions

Correct Answer: A. Accessibility considers travel time, cost and network conditions

Question 2

Which method generally provides the most behaviorally direct evidence of an existing retail trade area?

A. A fixed 5 km radius

B. A circular buffer around the property

C. Geocoded customer-origin records

D. The administrative boundary of the municipality

Correct Answer: C. Geocoded customer-origin records

Question 3

In the accessibility expression A(T) = ∑ⱼ Oⱼ I(tᵢⱼ ≤ T), what does the indicator function do?

A. It converts distance into population

B. It counts an opportunity only when the travel-time condition is met

C. It estimates the value of the property

D. It measures spatial autocorrelation

Correct Answer: B. It counts an opportunity only when the travel-time condition is met

Question 4

Which statement about a gravity model is correct?

A. It assumes that distance or travel cost has no effect on flows

B. It predicts flows using origin mass, destination attractiveness and separation

C. It can be applied only to residential property prices

D. It proves causation without calibration data

Correct Answer: B. It predicts flows using origin mass, destination attractiveness and separation

Question 5

A site has 18,000 households and a competing site has 15,000 households. Under min–max benefit normalization using only these two values, what is the site’s normalized score?

A. 0.15

B. 0.35

C. 0.50

D. 1.00

Correct Answer: D. 1.00

Question 6

Which issue occurs when spatial results change because data are grouped into different geographic units?

A. The modifiable areal unit problem

B. The sunk-cost problem

C. The replacement-cost problem

D. The capitalization problem

Correct Answer: A. The modifiable areal unit problem

Question 7

Which is an example of a development feasibility constraint rather than merely a locational amenity?

A. A nearby restaurant

B. A scenic view

C. Insufficient wastewater capacity

D. A popular shopping street

Correct Answer: C. Insufficient wastewater capacity

Question 8

In a weighted-sum location model, what should normally be true of the weights?

A. They should all be negative

B. They should sum to 1 when expressed as proportions

C. They must equal the property prices

D. They should be assigned after observing the preferred result

Correct Answer: B. They should sum to 1 when expressed as proportions

Question 9

Why should a planned transit station not automatically be treated as an existing accessibility benefit?

A. Transit never affects real estate demand

B. Planned infrastructure may be delayed, redesigned or cancelled

C. Stations can only serve industrial sites

D. Accessibility cannot be modeled spatially

Correct Answer: B. Planned infrastructure may be delayed, redesigned or cancelled

Question 10

Which practice is most appropriate when using sensitive mobility data in location analysis?

A. Publish individual travel histories

B. Use the data without documenting its source

C. Aggregate or anonymize data and apply privacy controls

D. Assume that large datasets have no ethical risks

Correct Answer: C. Aggregate or anonymize data and apply privacy controls