本研究探索的主題為物流績效指標在國際地理環境下是否具有空間依存性，同時以人均GDP做為國家富裕程度的資料，驗證與物流績效指標之間的相關性。據世界銀行公佈的資料，全球各國的物流績效指標，從分佈觀察發現，物流績效指標程度高的國家多為彼此相鄰，反之亦是，為了能夠實證物流績效指標空間依存性，本研究使用ArcGIS軟體進行探索性空間資料分析驗證物流績效指標是否符合本研究之假設，物流績效指標具有空間依存性。確立了物流績效指標有空間依存性後，再進一步運用空間計量經濟學提供的空間迴歸模型進一步分析探討各國家與的物流績效指標與鄰國之間的空間交互作用，並說明物流績效指標的空間資料特性，空間迴歸模型主要包括空間落遲模型(Spatial Lag Model)、空間誤差模型(Spatial Error Mode)以及空間Durbin模型(Spatial Durbin Model)。研究結果顯示，發現本研究資料的空間特性皆符合空間落遲模型、空間Durbin模型以及空間誤差模型分析結果，這代表一國家的物流績效指標會與鄰國物流績效指標之間具有相關性之外，同時鄰國的人均GDP也會與該國家物流績效指標有有關性，此也驗證了空間外溢效應存在。另外根據空間誤差模型結果表示，本研究藉由國家的富裕程度來解釋物流能力表現而言來說是不夠的，應還存有其他隱藏的變數使一國與鄰國的物流能力之間皆受到共同的影響。建議後續研究者可以深入探討尚未被發掘之隱藏變數，並觀察變數加入空間迴歸模型後分析結果的改變，並提供政策制定者能夠提升物流能力表現之建議方向。

This research aims to explore whether the logistics performance index (LPI) has spatial dependence in the international geographical environment and use the GDP per capita to verify the correlation with the LPI. Our research collect the World Bank published data. We discover the global LPI’s distribution express a phenomenon that is when the country has a high degree of LPI, the adjacent countries also has a high degree of LPI and vice versa. In order to demonstrate the LPI has a spatial dependence, this study uses Arcgis for exploratory spatial data analysis to verify that LPI is consistent with the hypothesis of this study. After determining the spatial dependency of LPI, we use the spatial regression model to further analyze the LPI’s spatial interaction between the country and its neighboring countries, as well as explain the spatial characteristics of LPI. The spatial regression model mainly includes Spatial Lag Model, Spatial Error Model and Spatial Durbin Model. The empirical results show that the spatial characteristics of the LPI are consistent with a Spatial regression model, which means that a country's LPI will be related to neighboring countries' LPI. While the GDP per capita of neighboring countries will also be related to the country's LPI, also verify the spatial spillover effect exist. Also, according to the results of the Spatial Error Model, only use the richness of the country to explain the performance of the logistics capacity is not enough. There should be other hidden variables that make the logistics capacity of a country, and its neighbors affected influence. Suggested that follow-up researchers can explore the hidden variables that and observe the changes in the results of the variables after adding the spatial regression model try to provide the direction of the policy makers to enhance the performance of the logistics capacity.

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