The objective of this study was to investigate the impact of political history on the dynamics of the interrelationship between land use and road networks within cities. Political history, in this study, is defined as the combination of the regional-level government programs and political events that affect the pattern of urbanization in a region. The study focused on urbanization in the cities of Pordenone and Gorizia, both situated in the Friuli-Venezia Giulia region of northeastern Italy, over the period 1950–2000. Being located adjacent to an international boundary, the city of Gorizia has a long history of political instability dating back to the beginning of the 20th century. It is assumed that this political instability has led to the application of differential socioeconomic policies which have affected the process of urbanization in the city. The aim of the study was to capture this effect by investigating the structural changes over the period of fifty years in both land use and the road network. In order to understand the extent of the effect Pordenone was used for comparison, since it has experienced a relatively peaceful past and regular growth. MOLAND (monitoring land-use/cover dynamics) data for land use and the road network were used for the study. Graph theory measures were used for a comparative analysis of the structural properties of road networks in both cities and their development over time. In order to understand the spatial relationship between change in land use and the road network, a nonparametric test of the spatial correspondence of areal distribution was used and tested at multiple spatial scales. The results suggest that political history does affect the land-use and road-network changes individually, but it did not affect the type of spatial relationship that exists between the two for those particular cities. This research makes a unique attempt to analyze the impact of policy on land-use and road-network change by using spatial data and methods of analysis which can help to understand their overall dynamics and that can be used as an alternative to data-intensive and time-intensive simulation models.
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