A worked spatial example

The example below demonstrates how different spatial questions produce different priorities. Four fictional settlements share a transport network around a proposed project. Expected additional residents, water capacity, and seasonal clinic access are examined separately.

All locations and values are synthetic. They do not describe a real investment or community. The example is a teaching tool, not an empirical forecast, an engineering assessment, or a validated risk model. Coordinates are a local schematic grid in kilometres, without a geographic coordinate reference system.

The planning question

Suppose a project assessment identifies four plausible receiving settlements. The client needs to decide where to verify service conditions, which accommodation alternatives to examine, and what to discuss with public authorities before mobilisation.

A population-only ranking would prioritise Junction. A proportional water-shortfall ranking would prioritise Riverside. A wet-season access test would draw attention to Hill and Riverside. Each finding answers a different question.

Change the scenario to compare the priorities. The bridge control tests a wet-season network closure; both controls change assumptions rather than estimate actual migration.

Figure 1. The same fictional geometry viewed through three planning questions. Labels give counts, percentages and minutes. Circle sizes have a separate scale in each panel; compare the labelled values across measures. Lines show a schematic network, and a dashed link indicates the selected wet-season closure.

Define the assumptions

The initial population is 7,000 across four settlements. A single planning period adds 6% background growth without the project. Additional residents in the central scenario total 1,900; lower and higher scenarios use 950 and 2,850. These are assumed net additional residents present during that period, rather than cumulative gross arrivals.

The water calculation uses 40 litres per person per day solely to demonstrate arithmetic. It is not a recommendation for water-system design or a statement of adequate household provision. Real demand analysis needs applicable standards, service levels, losses, seasonal availability, quality, non-household uses, and differentiated demand.

For simplicity, each settlement has a separate reliable delivered water capacity and there is no transfer between systems. No additional commercial water demand is included. The example does not estimate storage, hydraulic constraints, or costs.

The assumptions are in synthetic-example.json. A real application would attach a source and confidence assessment to every input, and separate observed capacity from planned upgrades.

Calculate demand and local deficits

For each settlement, multiply the population with the project by the assumed daily demand and subtract delivered capacity. Negative deficits become zero. Calculate the same quantity without the project to identify existing or background shortages.

For Gate in the central scenario:

Figure 2. Riverside already has a deficit under background growth. The central scenario increases that shortfall by 9,600 L/day. The chart follows the selected scenario; the table records the central case. Separating the components supports a clearer conversation about contribution and cost-sharing.

Gate has the largest absolute deficit, while Riverside has the largest deficit relative to delivered capacity. Either measure can inform a decision, but neither establishes health consequences or a sufficient mitigation measure. Household access, quality, affordability, and service reliability still need investigation.

See what aggregation conceals

Central demand totals 372,800 L/day and capacity totals 344,000 L/day. Subtracting regional totals gives a shortfall of 28,800 L/day. Adding the deficits of the separate systems gives 35,520 L/day.

The difference is Hill's unused 6,720 L/day, which cannot simply be reassigned in the model. Regional pooling assumes a transfer that the network and operations may not permit. A real project needs to establish whether such transfer is feasible, reliable, affordable, and environmentally acceptable.

The same issue arises with clinic staffing, school places, wastewater treatment, and rental stock. An aggregate surplus does not establish that affected people can access it.

Compare scenarios without calling them probabilities

The lower scenario still leaves Riverside short of water. A response that waits for central-scenario arrivals would miss that condition. The higher scenario substantially changes the scale of preparation needed at Gate and Junction.

These scenarios have no assigned probabilities. Their purpose is to test the resilience of a decision. Ask whether a proposed intervention performs adequately across plausible conditions and whether it can be expanded without creating new harm.

Model seasonal access through a network

The example assigns invented dry- and wet-season travel times to each network edge. Shortest paths are calculated to the clinic. The times are inputs chosen for teaching; they are not inferred from line lengths, terrain, or observed journeys.

The 30-minute test is arbitrary and is not a clinical access standard. It shows how a chosen threshold changes classification. In an actual assessment, establish the relevant service, transport mode, hours, affordability, and locally justified planning threshold.

WHO's AccessMod provides a fuller framework for physical accessibility, capacity, referrals, and expansion scenarios (WHO, AccessMod). This simplified network isolates one idea: distance alone does not represent seasonal access, and access alone does not establish that appropriate care is available.

To test a bridge closure, replace the wet-season cost for the Bridge–Riverside edge with null. The supplied functions then return null for disconnected travel times. Display that as “unreachable in model,” rather than zero minutes or a missing observation. A real investigation would look for alternative transport routes before concluding that access is absent.

Inspect and reproduce the calculations

This table follows the selected scenario and bridge setting. Direct labels, source assumptions and the fixed central-case tables above provide the numerical account alongside the graphics.

Download the assumptions, calculation module and JavaScript reproduction script. Keep the JSON in a data folder and both scripts in a sibling lib folder. With Node.js 24 or later, run from their parent folder:

node lib/reproduce-example.mjs > synthetic-results.json

The script calculates all 12 settlement–scenario records, verifies the totals and clinic journeys shown here, and checks the disconnected-route case. It uses the Node.js standard library; no geospatial packages or external data downloads are required. The same calculation module drives this notebook's Plot/D3 maps and bar charts.

For an immediate record of the current bridge setting, download all three scenarios below. The file includes the assumptions and the modified edge costs.

The schematic coordinates and supplied travel costs isolate network and aggregation effects. The spatial assessment article explains how to select, prepare and validate real geospatial evidence. The Framework JavaScript reference and Observable Plot documentation describe the native notebook rendering used here.

Translate the demonstration into a project inquiry

A real application would replace fictional populations with defined counts, arrivals with justified scenarios, capacities with verified delivery, and travel costs with observed or validated network models. Add uncertainty, household access, non-household demand, and environmental constraints.

The demonstration does not select a construction solution. It identifies questions: can Gate's demand be reduced through accommodation alternatives; what repair or expansion is feasible at Riverside; can Hill's wet-season access be improved without reallocating capacity away from other users; and how can government and client commitments be funded and verified?

Those questions connect the analysis to the management plan and to monitoring. A useful model makes the next decision clearer while preserving the evidence needed to challenge it.