Developing a city skyline for Hong Kong using GIS and urban design guidelines
Bibliographic record
Abstract
Abstract: Hong Kong is a metropolis with a dense population. The need to cope with rapid population growth under the constraint of limited amounts of developable lands has resulted in a concentrated development of high-rises. Enterprising developers have clearly exploited such behaviors in the urban development of Hong Kong. Buildings have been constructed to maximum heights whenever a height restriction was not specified in the authorization to build, or stretched to their limits sideways if given a height restriction. Both of these situations have given rise to either repetitive and monotonous roofs-capes or walls of regimental building flanking the harbor front. The objective of this research is to evaluate and redesign the of Hong Kong based on Urban Design Guidelines established by the Planning Department. The study makes use of the Geographic Information System (GIS) technology and its 3-dimensional (3D) modeling functions to construct, assess, and analyze the skyline. Introduction The term city skyline refers to a profile of buildings that forms the cityscape in daytime and the silhouette at night (Lim and Heath 1993). It comprises a group of tall buildings against the undulating backdrop of mountains enwrapping in a natural setting. City registers unique characteristics of a city's landscape shaped by planning controls, topographical conditions, commercial considerations, building design parameters, and environmental concerns. The cities of New York, San Francisco, Sydney, Shanghai, and Toronto are among major metropolitan cities in the world with uniquely identifiable skylines. Cities today are much more concerned about their images because a good impression is key to tourist attraction. Local governments of major cities have tried various means to enhance the visual quality of their skylines by exerting more control over building heights and design parameters, as well as by constructing more green corridors (Council of the City Vancouver 1997; Lower Manhattan Development Corporation 2002; United Kingdom Parliament 2003). The question is whether there are any standards to form judgment in our assessment of a skyline. What criteria or factors are used to form an opinion about a skyline? Are there objective methodologies to define these criteria? Aesthetic value has emerged as an important criterion for evaluating the quality of a (Delafons 1990; Habe 1989; Preiser and Rohane 1988). A great deal of focus in aesthetic interests concerns the height or the design quality of a building. Some writers have attempted to quantify the design quality of buildings with associated preferences. Stamps (1991) investigated the influence of height, complexity, and style on the preference of individual buildings, and concluded that relative complexity is a predictor of preference for individual high-rise buildings. Heath, Smith, and Lim (2000) investigated the effects of the silhouette and facade complexity of tall buildings on visual preferences of skylines. They found that a higher level of preference, arousal, and pleasure usually is associated with a higher silhouette complexity and facade intricacy. There is general agreement that buildings should not be considered in isolation but in reference to their unique topographical and landscape setting (Planning Department HKSAR 2001; Yu 2000; Bishop and Karadaglis 1997). Both man-made (including cultural and socioeconomic aspects) and natural (embracing mountains and waterbodies) contexts should exist coherently and in harmony with each another (Planning Department 2001). For instance, the should preserve some view corridors or breezeways to mountain backdrops or natural landscapes. Open spaces or green corridors between buildings should be protected to yield a cityscape of characteristic traits and visual aesthetics. This article outlines the criteria for development and assessment in Hong Kong. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".