本文選題：空間相互作用數(shù)據(jù)　切入點：起點終點流數(shù)據(jù)　出處：《華中科技大學》2016年博士論文　論文類型：學位論文

【摘要】：隨著位置感知技術的發(fā)展和廣泛應用,海量的空間數(shù)據(jù)變得越來越容易獲取�？臻g相互作用(Spatial Interaction)數(shù)據(jù),也被稱為起點終點流(Origin-Destination Flow)數(shù)據(jù),作為一種特殊的空間數(shù)據(jù),描述兩個地點之間的關聯(lián),或者兩個地點之間的物體移動。該數(shù)據(jù)在日常生活中非常普遍。例如:人的移民,車輛的行徑,動物的遷徙,還有疾病的傳播等等。大量空間相互作用數(shù)據(jù)的研究有助于理解不同領域的復雜動態(tài),包括城市規(guī)劃,智能交通,人口學,應急管理等等。例如,研究人類移動帶來的位置到位置的關聯(lián)程度,可以發(fā)現(xiàn)伴隨而來的病毒傳播的規(guī)律。目前的分析方法很難從大量的空間相互作用數(shù)據(jù)中發(fā)現(xiàn)有用的信息,也很難完全可視化大規(guī)模的數(shù)據(jù),這是數(shù)據(jù)本身的復雜性所決定的。數(shù)據(jù)本身的復雜性主要包括:(1)大的數(shù)據(jù)量:一個中型規(guī)模的數(shù)據(jù)集通常包含幾百到幾千個位置,很容易有幾千或者幾百萬位置與位置之間的關聯(lián)。(2)多種數(shù)據(jù)維度:這些數(shù)據(jù)集一般鑲嵌在多種維度內(nèi),比如空間維度,時間維度,網(wǎng)絡維度,多屬性維度。(3)可更改區(qū)域單元問題(Modifiable Area Unit Problem,MAUP):數(shù)據(jù)的起始位置的形狀和大小差異非常大,導致數(shù)據(jù)集的分析產(chǎn)生偏差。(4)多尺度問題:數(shù)據(jù)在不同的地理或時間尺度上展示不同的規(guī)律。對于這樣復雜的數(shù)據(jù)集,單一的方法不可能完全的解決這些問題。本文提出了一組方法來分析和可視化大規(guī)模的空間相互作用數(shù)據(jù),并有效地處理了上述的問題。這些方法主要包括:基于共享鄰居數(shù)的(Shared Nearest Neighbors)空間點聚類方法[1],該方法按照數(shù)據(jù)點分布的規(guī)律,將數(shù)據(jù)點歸納成含有相似數(shù)據(jù)量的聚類。在點分布密集的區(qū)域,形成小的地理空間的上的聚類,在點分布稀疏的區(qū)域,形成大的地理空間上的聚類。同時,這是一種數(shù)據(jù)驅(qū)動的聚類方法,能夠在數(shù)據(jù)之間找到自然分割。在聚類的結果的基礎上可視化統(tǒng)計量度,從而發(fā)現(xiàn)時空模式。該方法有效地處理了上述問題中數(shù)據(jù)量和可更改區(qū)域單元問題。基于共享鄰居數(shù)的空間相互作用數(shù)據(jù)層次聚類方法[2],該方法將傳統(tǒng)的層次聚類方法擴展運用到空間相互作用數(shù)據(jù)。該方法的主要思想是,將空間相互作用數(shù)據(jù)的起點間的相似度和終點間的相似度統(tǒng)一為一個相似性量度,然后運用聚合式的層次聚類方法將空間相互作用數(shù)據(jù)進行聚類。本文創(chuàng)新地使用共享鄰居數(shù)作為相似性量度,來應對數(shù)據(jù)在空間上分布不均勻的問題。文章在對空間相互作用數(shù)據(jù)聚類的基礎上,進一步研究了數(shù)據(jù)類群的時間特征。該方法能有效地處理上述問題中的前三個問題�？臻g相互作用數(shù)據(jù)核密度估計模型[3],這個方法的主要思想是將空間點的核密度估計方法擴展運用到空間相互作用數(shù)據(jù),對相互作用數(shù)據(jù)集進行密度的估計,然后從密度分布中提取特征。該方法將數(shù)據(jù)進行了高度的抽象,能夠很大程度上減少數(shù)據(jù)量。同時,核密度估計模型使用自適應的帶寬(Adaptive Bandwidth),可以很好的處理數(shù)據(jù)分布不均的問題(可更改區(qū)域單元問題)�；诳臻g相互作用數(shù)據(jù)核密度估計模型的多尺度可視化,該方法主要將空間相互作用數(shù)據(jù)核密度估計模型進一步擴展。使用不同的參數(shù)進行空間相互作用數(shù)據(jù)密度估計和代表性數(shù)據(jù)選擇,創(chuàng)新地實現(xiàn)了多辨率多尺度的流向地圖。該方法能夠非常有效的處理多尺度的問題。這些方法看似獨立,但是它們從不同的角度分析了空間相互作用數(shù)據(jù),用不同的方法增強了對復雜數(shù)據(jù)的理解。本文將多種不同但是互相補充的方法綜合到一起,將計算方法、可視化方法、可視化分析方法等結合在一起分析空間相互作用數(shù)據(jù),試圖將不同方法的不同角度綜合,形成一個全局整體的理解。
[Abstract]:With the development of Location Aware Technology and wide application of spatial data becomes more and more easy to get. The spatial interaction (Spatial Interaction), also known as the starting point and end point flow (Origin-Destination Flow) data, as a special kind of spatial data, describe the association between the two locations, moving objects or between two place. The data is very common in our daily life. For example: immigration, vehicle behavior, animal migration, and the spread of the disease and so on. A lot of research of spatial interaction data are useful in understanding the complex dynamic in different fields, including city planning, intelligent transportation, demography, emergency management and so on. For example correlation study of human, from a moving position to the position, can be found along with the spread of the virus to the law. The current analytical methods is very difficult from a large number of spatial interaction number According to the discovery of useful information, it is difficult to complete visualization of large-scale data, which is decided by the complexity of the data itself. The complexity of the data itself mainly includes: (1) a large amount of data: a medium-sized data sets often contain hundreds to thousands of locations, easy connection between thousands or millions of the position and position. (2) multiple data dimensions: These data sets usually embedded in multiple dimensions, such as spatial dimension, time dimension, network dimension, multi attribute dimension. (3) modifiable areal unit problem (Modifiable Area Unit Problem, MAUP) data: the starting position of the shape and size of the difference is very large the data set, analysis causes the deviation. (4) Multiscale Problems: data show different patterns in different geographical or time scales. For such complex data sets, a single method can not completely solve these The problem. This paper presents a set of methods to spatial analysis and visualization of large-scale interaction data, and effectively handle the problems. These methods include: Based on the number of shared neighbors (Shared Nearest Neighbors) [1] spatial clustering method, the method according to the data distribution, data points are summed up with clustering similar amount of data. In the distribution of dense area clustering form small geographical space on the distribution in sparse regions, cluster large geographical space. At the same time, this is a clustering method for data driven, can be found in the data between the natural segmentation based on clustering results. The visualization of statistical measure, in order to find the spatiotemporal pattern. This method effectively solves the problem of data and can change the unit number of shared neighbors. Spatial interaction based on data Hierarchical clustering method of [2], the traditional hierarchical clustering method is applied to the expansion of space interaction data. The main idea of this method is that the similarity of unified spatial interaction between the starting point and end point data similarity between a similarity measure, and then use the aggregation hierarchical clustering method of space interaction data type clustering. This paper using the number of shared neighbors as the similarity measure to deal with the data in a spatially inhomogeneous problem. Based on the spatial interaction data clustering, time to study the features of data groups. This method can effectively deal with the first three problems above problems in space. The interaction data of kernel density estimation model of [3], the main idea of this method is that the space point kernel density estimation method is extended to the spatial interaction of data The interaction data set of density estimation, and then extract features from density distribution. The data were highly abstracted, can greatly reduce the amount of data. At the same time, kernel density estimation using adaptive bandwidth model (Adaptive Bandwidth), can the problem of uneven distribution of data well (change the regional unit problem). Multiscale visualization of spatial interaction data model based on kernel density estimation method, the main spatial interaction data kernel density estimation model is further extended. Using different parameters of spatial interaction data density estimation and representative data selection, innovation to achieve a multi resolution multi-scale flow map. This method can deal with multi scale problem is very effective. These methods seem to be independent, but they are from a different point of view of the spatial interaction data, Using different methods to enhance the understanding of complex data. This will be different but mutually complementary methods together, the calculation method, visualization, visualization and analysis methods combined with analysis of spatial interaction data, to different methods with different angles to form a comprehensive, overall understanding.

【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：P208

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