Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships beten human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of b 2.0, i.e. the increasing amount of bsites with mainly user created content and social platforms opens up an opportunity to study such location's contexts.  investigates relationships existing beten human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.
['Zolzaya Dashdorj', 'Stanislav Sobolevsky']
I am writing a research paper on a idea Characterization of behavioral patterns exploiting description of geographical areas
[{"from": "human", "value": "I am writing a research paper on a idea Characterization of behavioral patterns exploiting description of geographical areas"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships beten human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of b 2.0, i.e. the increasing amount of bsites with mainly user created content and social platforms opens up an opportunity to study such location's contexts.  investigates relationships existing beten human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification."}]