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Using Tai Po as a case study, is clustering move evident in an older of newer shopping area?

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Introduction

Li Po Chun Geography Field Investigation Using Tai Po as a case study, is clustering move evident in an older of newer shopping area? Introduction: Shopping is the prime leisure activity for residents here in Hong Kong, a city of life where virtually every type of commodity can be purchased in you know where to look. So why are some retail outlets of the same category very clustered while others appear dispersed? Does this pattern really exist and is it more likely to occur in old or new shopping areas? The aim of this geography field investigation is to compare the distribution of selected retail types in two contrasting commercial areas in Hong Kong. This information is useful in finding patterns of distribution within the two areas and will give us an idea of the degree of clustering through the use of the NNI (Nearest Neighbor Index). Research Question The aim of this investigation will be to answer the following question: Using Tai Po as a case study, is clustering move evident in an older of newer shopping area? ...read more.

Middle

Shopping centers tend have a high degree of clustering and also a large range of retail types. This is because an indoor shopping mall such as Tai Po Mega Mall will likely be zoned according to retail types for the benefit of customers and also for management. For example, a certain floor in the center may be designated for a certain retail type (such as food). This leads to more competition as shops of similar retail types group can together for maximum profit. Shopping centers also tend to have a wider range of retail types as this attracts the large trade area needed for specialized retail types. In comparison, older districts have less clustering as shops tend to sell low order goods. Since this has a smaller profit turnover, shops in such areas tend to face less competition compared to newer shopping districts. Since shops in older districts tend to be well established with a loyal customer base, there is little room for similar retail types in close proximity, leading to less clustering. ...read more.

Conclusion

If the nearest neighbor happens to be on a different level (such as in the mall), the walking distance to the nearest escalator will be taken plus an additional 10 meters for going up. Once the raw data is collected, the mean distance for each retail type and the area of both regions shall be calculated and substituted into the NNI (Nearest Neighbor Index) for analysis. The NNI measures spatial dispersions of points (such as settlements) in a given area. These points are said to be either clustered or randomly / regularly dispersed. Clustered Random Dispersion Regular Dispersion The NNI Index is calculated as follows: 2D ? N A Key: N = Number of points A = Sampling area D = Mean between each point and its nearest neighbor This is calculated by finding (?d)/N where d = each individual distance. NNI values can be compared with the computer generated values to categorize the spatial dispersion: If the NNI is: ~0 = Clustered ~1 = Random >2 = Regular ...read more.

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