Towards Foreign Tourist at Heritage Sites Using GIS: A Case Study of Lahore
Bibliographic record
Abstract
Pakistan is potentially a good tourist destination for cultural and heritage overs because the Indus and Gandhara Civilizations, and a great Mughals heritage also exists in Pakistan particularly in Lahore city. Tourism is the fastest growing industry in modern world. This study is based on the number of foreign tourist who visited the heritage sites of Lahore. The purpose of the study is to examine the number of foreign tourist who visited the heritage sites of the Lahore at Shalimar Gardens, Lahore Fort and Lahore Museum and to find the tourism trend in Lahore whether it is declining or growing high. The secondary source of data was used and data was collected from archeology department, Lahore Museum and Pakistan Year Book of Statics 2014. Tools used for research purpose were Microsoft excel, Microsoft word and ARC GIS. The number of foreign tourists that visited Lahore fort were highest in 2006 i.e. 21,178 this is the highest number of tourists who visited Lahore Fort in 15 years and 2906 lowest number of foreign tourist who visited Lahore fort, at Shalimar Gardens highest number of foreign tourists was 8558 in 2000 and the lowest number was 309 in 2013 and at Lahore Museum in 7242 tourists is the highest number of Foreign tourist who visited museum in 2000 and the lowest number of foreign tourists that visited Lahore Museum was 1825 in 2010. The trend of foreign tourism in Lahore is declining after the incident of nine eleven due to terrorism. But now with the efforts of government and work of Walled city authority foreign tourism industry is growing in Lahore as it is the Queen of cities having major and attractive heritage places for tourist.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".