【摘要】：在人口的遷徙和流動中產(chǎn)生了大量的歷史數(shù)據(jù)，如何準確高效的利用這些數(shù)據(jù)得出具有政策導向意義的研究結(jié)果尤為重要。有理論表明人口的變動在相鄰或者相近的地域之間有較為明顯的相關(guān)性，但在以往的研究中多是單純從時間維度上考慮人口結(jié)構(gòu)的變動預測未來的走勢或是僅僅對人口的研究在空間統(tǒng)計的范疇內(nèi)進行分析。本文綜合考慮空間和時間的依賴性，針對兩類時空數(shù)據(jù)：連續(xù)數(shù)據(jù)與格數(shù)據(jù)，分別借助空間殘差模型和克里格地理統(tǒng)計模型兩個研究方法進行研究。 在理論方面，本文主要做了如下工作:對時空數(shù)據(jù)的類型介紹，時空數(shù)據(jù)建模的假設與前提，時空模型的形式，參數(shù)估計基本思想和極大似然的迭代方法，采用的似然比檢驗以及預測方法。同時也簡要討論了時空數(shù)據(jù)模型在實際中的應用；在應用層面借用以上的方法，通過R軟件編程實現(xiàn)了整個計算過程。在實證部分中，先對數(shù)據(jù)進行描述性分析，初步了解其空間和時間維度的分布特點。同時做了統(tǒng)計解釋并根據(jù)數(shù)據(jù)的一階差分特征判定它是適用于空間殘差模型的，接下來進行模型擬合和檢驗發(fā)現(xiàn)其空間和時間依賴系數(shù)都顯著，為人口分布的空間依賴理論提供了數(shù)理方面的佐證，并對其進行實際意義的解釋。而后對兩種模型的擬合結(jié)果做出了預測精度和計算效率的對比和評價，發(fā)現(xiàn)針對此問題，時空克里格方法在預測精度上優(yōu)于空間誤差模型。以上研究內(nèi)容為時空數(shù)據(jù)模型的分析提供數(shù)理方面的分析思路。 本文在進行實證分析時采用的數(shù)據(jù)是瑞典的人口數(shù)據(jù)。因為其空間分辨率高，以教區(qū)為地理單位，比省市地區(qū)的數(shù)據(jù)精度要高，有助于在時空分析中得出準確的結(jié)論，并且在時間維度上具有完整性和分割一致性。而國內(nèi)的人口數(shù)據(jù)主要通過人口普查得到的省市縣的數(shù)據(jù)，在數(shù)據(jù)的可獲得性和空間分辨率上有局限，并且在時空數(shù)據(jù)模型使用之前，需要對其進行空間化的處理，考慮到工作量和時間成本因素，，因而本文直接選取了直接可用來分析的瑞典人口時空數(shù)據(jù)。但只要數(shù)據(jù)質(zhì)量夠高，或者數(shù)據(jù)空間化的預處理已經(jīng)完成，在遇到時空數(shù)據(jù)模型問題時，本文的研究思路是值得借鑒和參考的。
[Abstract]:A large number of historical data have been produced in the migration and flow of population. How to use these data accurately and efficiently to obtain policy-oriented research results is particularly important. There are theories that there is a clear correlation between population changes in adjacent or close regions, However, in the previous studies, it is only from the time dimension to consider the change of population structure to predict the future trend, or only to analyze the population research in the field of spatial statistics. In this paper, considering the dependence of space and time, two kinds of spatiotemporal data, continuous data and lattice data, are studied with the help of spatial residuals model and Kriging geographic statistical model, respectively. In theory, this paper mainly introduces the types of spatiotemporal data, the assumptions and premises of spatio-temporal data modeling, the form of spatio-temporal model, the basic idea of parameter estimation and the maximum likelihood iterative method. The likelihood ratio test and prediction method are used. At the same time, the application of spatio-temporal data model in practice is briefly discussed, and the whole calculation process is realized by using the above methods in the application level. In the empirical part, firstly, the data are analyzed descriptive, and the spatial and temporal distribution characteristics of the spatial and temporal dimensions are preliminarily understood. At the same time, the statistical explanation is made and the first order difference characteristic of the data is determined to be suitable for the spatial residual model. Then, the model fitting and testing are carried out and found that the spatial and time dependent coefficients are significant. It provides mathematical evidence for spatial dependence theory of population distribution and explains its practical significance. Then the prediction accuracy and computational efficiency of the two models are compared and evaluated. It is found that the spatial-temporal Kriging method is superior to the spatial error model in prediction accuracy. The above research content provides the mathematical analysis thought for the time-space data model analysis. The data used in this paper are Swedish population data. Because of its high spatial resolution, the parish is a geographical unit, which is more accurate than the data of provinces and cities, which is helpful to draw an accurate conclusion in time and space analysis, and has integrity and segmentation consistency in time dimension. However, the data of provinces, cities and counties, which are mainly obtained from the population census, are limited in terms of data availability and spatial resolution, and they need to be spatially processed before the use of spatio-temporal data models. Considering the factors of workload and time cost, this paper directly selects the space-time data of Swedish population which can be directly used to analyze. However, as long as the data quality is high enough, or the preprocessing of data spatialization has been completed, the research idea of this paper is worthy of reference and reference when we encounter the problem of spatiotemporal data model.
【學位授予單位】：首都經(jīng)濟貿(mào)易大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：C921

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