A study for location of store-based retail in urban areas : a case study of Apparel market areas in Kanpur
Bibliographic record
Abstract
Since the last quarter of 20th century, Globalization emerged as an important phenomenon across the world because of its exponential growth and taking over many countries. It calls for trade of products and services from any part of world to another part in a smooth way. This provides investors an opportunity to invest in new emerging markets and economies. Retail is one of the biggest investment sector showing immense potential. For a systematic investment and recovery framework, it needs to be organized. Organized retail can also be categorized into various types in which store-based retail pops out as a major one. It is globally accepted that Indian markets has a huge potential for investment because of its unique demographic profile of nearly 1.3 billion people. Earlier Indian economy was closed and due to lack of huge investment opportunities, Indian markets were led by informal/unorganized sector. Indian economic reforms took place on 1991 which opened paths for investment opportunities in India. With over 92 percent of the business coming from unorganized sector, Indian retail sector offers immense potential for growth and consolidation. The revenue generation from organized retail was INR 0.9 trillion (USD15.5 Billion) in 2009, INR 2.4trillion (USD41.4 billion) in 2012, and is expected to continuously growing at an impressive rate to a projected INR 5.5 trillion (USD94.8billion) by 2019. (KPMG, 2014) Indian economy is drastically changed from the era of economic reforms which took place more than 25 years ago. There are many demand and supply factors which are presently leading the direction of Indian markets. But the success of a retail store cannot be completely articulated from these factors. From various decades of studies, it is found that location is the most important factor for articulating the success or failure of a retail store. A store’s location is one of the most important decisions a retailer must make, as it typically involves substantial financial outlay and long-term commitment. Location is a major cost factor- It involves large capital investment. It affects transportation cost.A study for location of store-based retail in urban areas: A case study of Apparel market areas in Kanpur ii It affects Human resource cost. Location is a major revenue factor- The volume of business depends on location. A prime location gives advantage over Competitors. It effects the consumers’ traffic. In previous years, a growing interest can be seen among the academic world and the private organizations for using GIS techniques in the investigation and planning of retail stores network. Almost all the various retail establishments have felt the need to plan for approaching consumer markets and compete with established competitions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".