Bern's positioning strategies: Escaping the fate of a secondary capital city?

Bern is a classic example of a so-called secondary capital city, which is defined as a capital city that is not the primary economic center of its nation. Such capital cities feature a specific political economy characterized by a strong government presence in its regional economy and its local governance arrangements. Bern has been losing importance in the Swiss urban system over the past decades due to a stagnating economy, population decline and missed opportunities for regional cooperation. To re-position itself in the Swiss urban hierarchy, political leaders and policymakers established a non-profit organization called “Capital Region Switzerland” in 2010 arguing that a capital city should not be measured by economic success only, but by its function as a political center where political decisions are negotiated and implemented. This city profile analyses Bern's strategy and discusses its ambitions and limitations in the context of the city's history, socio-economic and political conditions. We conclude that Bern's positioning strategy has so far been a political success, yet that there are severe limitations regarding advancing economic development. As a result, this re-positioning strategy is not able to address the fundamental economic development challenges that Bern faces as a secondary capital city.

Classifying and mapping the urban transition in Vietnam

The urban transition almost always involves wrenching social adjustment as small agricultural communities are forced to adjust rapidly to industrial ways of life. Large-scale in-migration of young people, usually from poor regions, creates enormous demand and expectations for community and social services. One immediate problem planners face in approaching this challenge is how to define, differentiate, and map what is rural, urban, and transitional (i.e., peri-urban). This project established an urban classification for Vietnam by using national census and remote sensing data to identify and map the smallest administrative units for which data are collected as rural, peri-urban, urban, or urban core. We used both natural and human factors in the quantitative model: income from agriculture, land under agriculture and forests, houses with modern sanitation, and the Normalized Difference Vegetation Index. Model results suggest that in 2006, 71% of Vietnam's 10,891 communes were rural, 18% peri-urban, 3% urban, and 4% urban core. Of the communes our model classified as peri-urban, 61% were classified by the Vietnamese government as rural. More than 7% of Vietnam's land area can be classified as peri-urban and approximately 13% of its population (more than 11 million people) lives in peri-urban areas. We identified and mapped three types of peri-urban places: communes in the periphery of large towns and cities; communes along highways; and communes associated with provincial administration or home to industrial, energy, or natural resources projects (e.g., mining). We validated this classification based on ground observations, analyses of multi-temporal night-time lights data, and an examination of road networks. The model provides a method for rapidly assessing the rural–urban nature of places to assist planners in identifying rural areas undergoing rapid change with accompanying needs for investments in building, sanitation, road infrastructure, and government institutions.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.