Browsing by Author "Marasinghe, L"

Now showing 1 - 2 of 2

Embargo
Analyzing the Location Feasibility for Retail Businesses using Market Location Factors
(IEEE, 2018-12-21) Marasinghe, L; Rupasinghe, M; Kumarasinghe, B; Perera, M; Thelijjagoda, S
The retail industry is a fast growing and a highly revenue generating industry. The location of a retail outlet is the most influencing factor for the success of the business. Therefore selecting a location for a retail store or an outlet is a challenging process. The purpose of this study is to define a method and develop a system to analyze the feasibility of a selected location for a retail store. The factors used in this method are location and market factors of a selected area. In order to define and test the method, we selected three different areas and five different retail store types. To retrieve location data, we used Google Maps web service. Consumer surveys were conducted in selected areas to get information about consumers' shopping patterns and selections. From the web service, we were able to identify transport modes, locations of competing stores and shopping areas. The findings of this study and the method described is useful in deciding the feasibility of any given location for a retail outlet. Also the specified method and model can be modified and extended to analyze different kinds of business locations.
Embargo
Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry
(Institute of Electrical and Electronics Engineers, 2022-10-22) Balasooriya, T; Kavishka, L; Dananjana, I; Yumna, M; Thelijjagoda, T; Attanayaka, B; Marasinghe, L
Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized insights and alternative locations are generated via the personal preference-based suitability analyzer and the price prediction system provides the current and future price fluctuations. The usefulness of image processing technologies and machine learning for making the sellers' journey easier on a real estate platform has also been assessed.