In this article, we examine a relationship between population/economic size and resilience of Czech regions. More specifically, we ask if there are any significant differences among metropolitan cores and hinterlands, urban regions and rural regions in (post)crisis economic development in the period 2009–2013. Three aspects of resilience were considered: volatility of unemployment, renewal (increase in economic performance compared to other regions) and reorientation (measured by the intensity of structural changes in total employment). We found relatively small differences among particular types of regions and high intra-group heterogeneity. Specialized industrial urban regions exhibited the fastest economic growth in the (post)crisis period. Metropolitan cores lagged slightly behind, but experienced relatively stable economic development. Although rural regions exhibited the highest unemployment volatility, they did not lag behind in terms of value added growth. Regional resilience in a small open economy like Czechia seems to be predominantly driven by extraregional factors such as the position in global production networks and economic performance in particular industries or large transnational corporations.

In this paper, we focus on the question of how particular types of regions react to economic shocks. Drawing on a case study of Czech microregions, we aim to determine if the old industrial regions differ significantly from other types of regions (metropolitan, urban, rural) in (post)crisis economic development. Two aspects of regional resilience (measured by unemployment growth) were considered: recession and recovery. We focused on the comparison of two subsequent recessionary shocks: the 2008–2010 global recession and the 2012–2013 austerity crisis. Metropolitan regions showed relatively stable economic development. Old industrial regions were more resilient than expected. Rural regions exhibited highly diverse reactions: lower resistance was characteristic for regions specialized (mostly) in automotive and some labour-intensive industries. The first wave of recession increased unemployment mostly in (rural) regions dependent on export-oriented manufacturing, the second wave especially hit larger cities with metropolitan functions. Regions that experienced a sharp unemployment increase in the first recession were generally more resistant in the second wave. All types of regions were internally highly diverse in their reactions to economic crises, which points to the importance of micro-regional variations of economic resilience, the performance of individual local firms and subsidiaries, and extra-regional factors of regional development.

After decentralization, there was massive development in Jakarta megacity’s outer suburbs (JMOS), especially in Bekasi and Tangerang regencies, marked by large-scale industrial estate/park (LSIEP) and followed by new town developments. However, this process led to the emergence of “chaotic” urban-rural land patterns. This study sought to identify the extent to which LSIEP development has affected rural-urban land transformation (RULT). The primary data were land use/cover (LUC) data from 2005, 2015, and 2020 and the LSIEP distributions. The methods applied are the Patch-generating Land Use Simulation (PLUS) model for 2025’s LUC prediction and the RULT index approach, RULT index development using the analytical hierarchy process. These combined approaches were novel in Indonesia, which usually relies on Cellular Automata (CA)-Markov, overlay (spatial), and descriptive statistics analyses to describe the RULT phenomenon. It was found that the villages located around the LSIEP close to the Jakarta megacity toll road network and those adjacent to the municipality (city) had been transformed into urban areas, while villages far from those locations were still rural. This study’s results help clarify the rural to urban transformation in Jakarta megacity’s outer suburbs and could be used as input for spatial planning policy.

In this paper, we aim to describe and explain the regional disparities in economic resilience in Slovakia in the period 1997–2017. We focus on the effects of economic structure in combination with the vertical (potential accessibility) and horizontal geographical location. Since the early 1990s, Slovak (non-)metropolitan regions exhibited deep changes in the sectoral structure of the economy that were followed by sharp unemployment increases. Due to the FDI-fueled economic growth in the last two decades, however, considerable progress in regional economic growth and reduction in unemployment was recorded. Therefore, Slovak non-metropolitan regions provide valuable lessons for the analysis of regional economic resilience in a long-term period. We ask if, and to what extent were, the prospects of regional renewal after economic crises associated with the geographical location, economic diversity, firm size and sectoral structure of the economy. We employed spatial regression models to test the effects of the potential accessibility, horizontal geographical location and industrial diversity, and sectoral (agriculture, manufacturing) and firm size structure. The dependent variable, Economic Resilience, was measured by the Regional Development Index, combining the indicators of demographic ageing, net migration, income per capita and registered unemployment rate. Potential accessibility and horizontal geographical location were the key predictors of regional economic resilience. Districts with tertiarized and diversified industrial and firm size structures scored, on average, higher in RDI than specialized districts with large firms and/or a high share of agriculture/manufacturing in total employment.

We compare intra-urban localization patterns of advertising and IT companies in three large Czech cities. The main aim of our analysis is an empirically-based contribution to the question to what extent do knowledge bases affect the spatial distribution of various knowledge-intensive business industries. The central research question is: To what extent is the localization of these two industries influenced by different modes of innovation/knowledge bases (symbolic vs. synthetic) and to what extent by contextual factors, such as urban size, morphology, position in the urban hierarchy and economic profile of the given city. We found that the urban contexts shape the localization patterns of advertising and IT companies more than differences in knowledge bases—both industries cluster primarily in the inner cities and urban cores. Formation of more suburban IT “scientific neighborhoods” is limited.