Questions 31–40 Complete the notes below. Write ONE WORD ONLY for each answer.
City Shade Modelling
• uses computer [31] ______ to predict shade in streets and parks
• helps planners reduce heat stress and improve walkability
• is most useful when linked to real design decisions
Problems in creating reliable shade models
• Tree cover is uneven, and new [32] ______ take years to grow
• Tall buildings can create an urban [33] ______ effect that changes sunlight patterns
• Many cities lack accurate height data without [34] ______ surveys
• Results depend on the chosen map [35] ______
• Surface colour matters, because high [36] ______ surfaces reflect more sunlight
• Models often ignore how [37] ______ actually move through the city
• Shade changes quickly around [38] ______, so timing is critical
Solutions
• Use open standards so different departments can share model outputs
• Test proposed changes before a street [39] ______ project begins
• Include community input to improve [40] ______ in shade investment
Part 4: You will hear a lecturer explaining City Shade Modelling and outlining the main difficulties and proposed solutions.
Hello everyone. Today I will talk about City Shade Modelling. This is an approach that uses data and computers to estimate where shade will fall across a city at different times of day and in different seasons. The reason it matters is simple: shade affects how hot people feel when they walk, wait for a bus, or sit in a park. In hotter climates, and increasingly in temperate climates too, shade is becoming an important part of urban health planning.
City Shade Modelling often begins with a computer simulation. A simulation is a simplified model of reality that we can run repeatedly. It uses information about buildings, trees, street width, and the position of the sun. With this, we can estimate which pavements will be shaded at nine in the morning, at midday, and later in the afternoon. Planners use the results to decide where to plant trees, where to place shelters, and how to design public spaces for heat comfort. It can also support walkability, because shaded routes feel easier to use on warm days.
However, producing reliable shade models has several difficulties. One basic issue is that tree cover is uneven. Some districts have many mature trees, while others have almost none. Even if a city plants more trees, new trees take years to grow, so the benefit is not immediate. That means a plan based only on planting may not help residents during the next few summers. For that reason, many cities also consider built shade, such as awnings, pergolas, or shade sails.
Another challenge is the role of buildings. Tall buildings can create an urban canyon effect. Imagine a narrow street with high buildings on both sides. Sunlight can be blocked for long periods, but it can also be reflected and concentrated depending on street direction and materials. The canyon effect changes sunlight patterns in ways that are not obvious from a simple map, so a shade model needs accurate building footprints and realistic street geometry.
To get that accuracy, we need good height data. Many cities have basic land maps, but they do not always include precise building heights or tree heights. Without detailed height information, the model becomes rough and small errors multiply. In practice, many cities lack accurate height data without lidar surveys. Lidar uses laser scanning to measure surfaces, capturing roofs and tree canopies in fine detail.
Even with good data, results depend on settings. One important setting is map resolution. Resolution refers to how detailed the grid is. If the grid is too large, small shaded areas disappear. If it is too small, the model becomes slow and expensive to run across an entire city. Analysts have to choose a resolution that balances detail and speed.
Surface materials also matter. Shade is not the only factor in heat comfort. Surface colour and material affect temperature. A dark road absorbs heat, while a lighter surface reflects more. In modelling, surface reflectivity is often linked to albedo. High albedo surfaces reflect more sunlight and can reduce surface temperatures, but they may also increase glare.
A further limitation is human behaviour. Models often assume people take the shortest route. In reality, pedestrians choose routes based on crossings, safety, shops, and habit. Many models ignore how pedestrians actually move through the city, so shade investments may miss the busiest walking routes.
Timing is another key factor. Shade is dynamic. A place that is comfortable at ten in the morning might be exposed at one in the afternoon. Shade also changes with season. In many cities the most dangerous heat occurs in early afternoon, but shade changes quickly around noon, so the time setting in the model is critical.
We also need to check model outputs against reality. Field surveys, photos, and sensors can show whether predicted shade matches what people experience on the ground. Without this kind of validation, a model may look good but still mislead planners when decisions are made.
Now, what solutions are being used? First, many cities are adopting open standards so departments can share model outputs. Second, cities use shade models to test proposals before construction begins. This is especially useful before a street retrofit project begins, because retrofits are expensive and disruptive. Finally, cities are starting to include residents in the process, because shade is also a fairness issue. Including community input can improve equity in shade investment.
To conclude, City Shade Modelling is a practical tool for healthier cities, but it depends on data quality, realistic assumptions, and good decisions.
