استفاده از فضاي فعاليت به منظور مطالعه متغيرهاي مؤثر بر رفتار حركتي انساني

Using Activity Space to Investigate the Effective Variables on Human Movement Behavior

حركت يكي از اساسي­ ترين مؤلفه­ هاي­ زندگي افراد به شمار مي­آيد كه اطلاعات مفيدي درباره نحوه حركت مردم از آن قابل استخراج است. امروزه با در دسترس بودن حجم زياد داده ­هاي حركتي، مطالعات فراواني در زمينه حركت انسان­ها انجام شده است، كه از آن جمله مي­توان به مدلسازي متغيرهاي مؤثر بر رفتار حركتي انسان به منظور درك بهتر نحوه حركت مردم و حل بسياري از مسائل اجتماعي، سياست­گذاري­ها، تصميم­ گيري­ها، طراحي شهري، كنترل حمل و نقل و كاهش ترافيك اشاره كرد. يكي از پيش­نياز­هاي اساسي مطالعه رفتارهاي حركتي انساني، نحوه مدل سازي حركت در قالب پارامترهايي است كه بتوانند چگونگي حركت را نشان دهند تا در مرحله بعد بتوان تأثير مؤلفه­ هاي مختلف بر روي حركت را به كمك آنها نشان داد. براي اين منظور، اين تحقيق در نظر دارد با معرفي مفهوم فضاي فعاليت (قسمتي از فضا كه فرد در آن­ فعاليت دارد) و چگونگي كمي­سازي آن، از اين پارامتر براي نمايش خط سير افراد و پارامترهاي مرتبط با آن و سپس مدلسازي متغيرهاي مؤثر بر رفتار حركتي انساني استفاده كند. نتايج بكارگيري اين رويكرد براي تحليل­ تأثير برخي متغيرهاي جمعيتي-اجتماعي (مانند سن، جنسيت و شغل) و زماني (مانند ماه­هاي سال، روزهاي هفته و تعطيلات) بر روي رفتارهاي حركتي افراد با استفاده از يك مجموعه داده موقعيتي بدست آمده از تلفن همراه افراد در كشور سوييس (كه از اين به بعد داده تلفن همراه ناميده مي­شود)، حاكي از كاركرد مطلوب آن براي هدف مورد نظر مي­باشد.

Movement as “a change in the spatial location of the whole individual in time” is the result of complex states and behaviors of moving entities or processes. “Movement data link together space, time, and objects positioned in space and time. They hold valuable information about moving objects, properties of space and time as well as events and processes occurring in space and time”. Individuals’ movements are measurable responses to the combination of internal states, physiological constraints, and environmental parameters, which control most of the essential behaviors of that entity or phenomena. Therefore, it could be the base for analyzing the behavior of entities or movement phenomena in spatio-temporal spaces. The importance of studying the behavior of people and the expansion of access to spatial data has led to development of activities related to study of movement of individuals as well as discovery of patterns and behavior of individuals for better to use in urban planning and policymaking. Understanding the relationship between demographic and temporal variables and human movement as well as extraction of behavioral patterns is essential assess different social issues. for example, understanding the way that people move, amount of their movement, and their internal interactions can lead to better understanding of different social issues such as spreading a special disease, locating infrastructures and city management, reducing traffic, and structure of urban communities. In this research, activity space is used to extract the relation between demographic and temporal variables and mobility behavior of people. It is simply the environment or area within which the user moves so it indicates dispersion of the places that user visits. It actually constructs a structure for the places visited by the user. Investigating the activity space helps to infer characteristics of the users’ movement and understand the difference between their mobility behaviors. It is a frequently used measure “that captures individual and environmental differences and offers an alternative approach to study the spatial reach of travelers”. Among three available types of activity space (i.e. standard deviation ellipse, minimum convex polygon and daily path area), the standard deviation ellipse is used because it focuses on general shape and direction of the activity space “without introducing potential error introduced by using geographically distant points”. Determinants of the activity space (such as area, shape index, radius, entropy and ratio) represent the characteristics of individual’s movement behavior. This research aims to explore a Swiss human movement sample dataset, called MDC, in order to discover the effect of demographic and temporal variables on human movement patterns in Switzerland. Data analysis and comparison of results indicate that age, working and time are decisive demographic and temporal variables for area and shape index of the activity space so they are useful for understanding some of the human’s movement characteristics. The results declare that middle age users, females and people who work have more active mobility pattern, since they have higher area and shape index than users in other groups. Moreover, analyzing the standard deviation of area and shape index of the activity spaces for time groups indicates that people tend to travel further in weekends and summer than weekdays and winter.