本文選題：社交媒體　切入點：數(shù)據(jù)挖掘　出處：《武漢大學》2017年碩士論文　論文類型：學位論文

【摘要】：人類的活動影響著交通、住房、商業(yè)、文化、基礎(chǔ)設(shè)施建設(shè)等與城市發(fā)展相關(guān)的方方面面,認識并了解人類的活動有助于城市的規(guī)劃與建設(shè)。近年來,基于位置的社交媒體平臺的發(fā)展,使人們的生活從現(xiàn)實世界延伸到虛擬世界中,人們在現(xiàn)實世界中活動時在部分時間里也同時活動于虛擬世界里,位置和時間是聯(lián)系兩個世界的橋梁,人們在虛擬世界中活動時留下了在現(xiàn)實世界所處的位置和時間信息。我們可以根據(jù)用戶在虛擬世界中留下時空信息研究人類在現(xiàn)實世界中的移動規(guī)律�，F(xiàn)實生活中的絕大多數(shù)人都有固定的生活節(jié)奏,因此其活動都有一定的規(guī)律可循,然而由于生活的復雜性,人們在遵循規(guī)律性活動的同時也進行了一些偶然性活動。如何從偶然活動中提取規(guī)律性活動成為研究人類移動規(guī)律的一個挑戰(zhàn)。本文基于社交媒體數(shù)據(jù),使用擴展后的Markov模型研究了人類各類活動所占比重,并分析了群體的活動對城市人口流動的反映。之后使用時空路徑理論提取了人類主要的活動模式,并使用聚類算法根據(jù)活動模式將用戶劃分到不同類別,研究了某些類別具有的時空特征。本文所做主要工作如下:1)基于Markov模型中狀態(tài)轉(zhuǎn)移思想,將時間維度加入到模型中來,研究人類在不同時段出現(xiàn)在不同位置以及在位置間移動的可能性,包括:人類移動提取,人類移動位置出現(xiàn)探測、人類移動位置轉(zhuǎn)換探測,綜合預測算法設(shè)計。2)將個體移動規(guī)律的探測方法應用到群體移動規(guī)律探測中,使用活動位置出現(xiàn)概率反映城市人群在不同時刻的聚集狀況,使用活動位置轉(zhuǎn)換概率反映人群在不同時刻的流動情況。并使用ECharts動態(tài)展示人群的動態(tài)活動情況。3)將時空路徑理論應用到人類的長期的主要活動模式探索中來,研究人類移動熱點的提取及聚類方法,時空路徑生成及路徑出現(xiàn)概率計算。使用參數(shù)組合生成了用戶多條時空路徑,獲得用戶更全面的活動模式。將時空路徑在以24小時為Z軸的三維空間中展示以獲得人類于一天內(nèi)的移動規(guī)律及移動規(guī)律隨時間的變化。4)不同用戶的時空路徑具有不同的時空特征,代表了不同的活動模式。設(shè)計時空路徑聚類方法,將不同時空特征的時空路徑劃分到不同類別,研究不同類別用戶具有的時間和空間上的規(guī)律。
[Abstract]:Human activities affect transportation, housing, commerce, culture, infrastructure construction and other related aspects of urban development, understanding and understanding of human activities contribute to urban planning and construction in recent years, With the development of location-based social media platform, people's life extends from the real world to the virtual world. Location and time are bridges between the two worlds, When people move in virtual world, they leave the information of their position and time in the real world. We can study the law of human movement in the real world according to the time and space information left by the user in the virtual world. Most of them have a fixed rhythm of life, So they all have certain rules to follow, but because of the complexity of life, While following regular activities, people have also carried out some accidental activities. How to extract regular activities from accidental activities has become a challenge in studying the laws of human mobility. This paper is based on social media data. The proportion of various human activities is studied by using the extended Markov model, and the reflection of group activities on urban population flow is analyzed. Then, the main human activity patterns are extracted by using space-time path theory. We use clustering algorithm to divide users into different categories according to their activity patterns, and study the space-time characteristics of some categories. The main work of this paper is as follows: 1) based on the idea of state transition in Markov model, the time dimension is added to the model. To study the possibility that human beings appear in different positions at different times and move between positions, including: human movement extraction, human mobile position detection, human mobile position conversion detection, The synthetic prediction algorithm design. 2) apply the detection method of individual movement law to the detection of group movement law, and use the probability of occurrence of activity position to reflect the gathering state of city crowd at different time. The temporal and spatial path theory is applied to the exploration of human's long term main activity pattern, using the probability of changing the activity position to reflect the movement of the crowd at different times, and using ECharts to show the dynamic activity of the crowd dynamically. The extraction and clustering methods of human mobile hot spots, the generation of space-time paths and the calculation of path occurrence probability are studied, and the multiple spatio-temporal paths of users are generated by using the combination of parameters. Obtain a more comprehensive user mode of activity. Display the space-time path in a three-dimensional space with the Z axis of 24 hours to obtain the movement laws of human beings within a day and their movement laws over time. 4) the space-time paths of different users. Have different space-time characteristics, The method of spatio-temporal path clustering is designed to divide the spatio-temporal paths of different spatio-temporal characteristics into different categories and to study the temporal and spatial laws of different types of users.
【學位授予單位】：武漢大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：C912.1;P208

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