Industrial land use planning in a context of development of high tech industry : a case study of the city of Richmond
Bibliographic record
Abstract
This thesis examines city conditions that attract high tech companies in Canada and the United States, gives an insight into the high tech industry in Richmond, analyses factors that determine the location of high tech in the City of Richmond, and identifies possible actions that the City of Richmond could take to attract high technology. The problem statement focuses on factors that determine the location of high tech industries in North American cities, generally, and in the City of Richmond, specifically. The thesis' methodology consists of a literature review, survey and interview research, statistical analysis, and policies and zoning regulations review. The economic and planning literature have provided better understanding of the definition of high tech industry, the types of high technology clusters, and the locational factors affecting the spatial distribution of high tech companies. Both the survey and the interview questions have focused on the effective methods of attracting high tech industries, the conditions making high tech industries successful in the surveyed cities, the development characteristics supporting high tech industries, the development tools used by the cities in practice, and the current effective planning approaches to high technology. The statistical analysis and the review of policies and zoning regulations have provided an insight into the number and size of the high tech companies located in the city of Richmond, as well as into Richmond's policies related to high technology. This thesis finds that high tech industries are foot - loose industries that often change locations due to factors, such as better tax climate, availability of an educated workforce, and post - secondary institutions presence. As a result, there is no standard framework to explain the locational pattern of high technology. However, this thesis identifies two sets of locational factors - general and specific - that are decisive for high technology today. There is an obvious requirement for general locational factors, such as a skilled workforce, post-secondary educational institutions proximity, linkages to other industries, good public transit, good quality business parks, higher building densities, and affordable housing. The specific locational factors depend on the type of high tech sector, and they are required by certain high tech sectors, such as biotechnology. The locational factors revealed by this thesis do not represent a framework that is applicable to all communities that aim to attract high tech businesses. However, the findings of this thesis present valid information for any community to consider before pursuing high tech policies and programs. In addition, this thesis leads to recommendations regarding the steps that communities could undertake in order to develop successful high tech policies and programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".