Spatial analysis of regional industrial clusters in the German forest sector

Kies, Uwe; Mrosek, T.; Schulte, Andreas

doi:10.1505/ifor.11.1.38

Cited by 19 publications

(12 citation statements)

References 22 publications

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“…The two metrics perform quite similarly but differ in that Getis-Ord Gi* merely identifies high and low clusters (in this case, high and low frequencies of atom/arc usage) while Local Moran's I identifies both clusters and outliers (Kies et al 2009). Each test measures spatial autocorrelation by comparing the observed instances to what would be expected in a randomized environment and returns a corresponding Z score for each element that specifies its degree of autocorrelation.…”

Section: Spatial Methodsmentioning

confidence: 98%

Role of Spatial Data in the Protection of Critical Infrastructure and Homeland Defense

Yates

Casas

2010

Appl. Spatial Analysis

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This paper examines how the use of spatial analysis techniques and geographic information systems may assist in developing a more complete understanding of underlying solutions trends and properties in complex optimization problems. In the model, an attacker with multiple attractive targets and multiple entry points into the region attempts to find a complete network path offering the highest probability of non-detection by a defender who has allocated detection resources to reduce arc non-detection and who wishes to minimize this maximum attacker path (this problem is a direct variant of the shortest path network interdiction problem). This paper introduces spatial analysis techniques to analyze defender resource allocation and attacker path selection in two sample networks (Lancaster-Palmdale, California and Northridge, California). The resulting discussion begins to examine how network diversity and other problem parameters can impact observed defender/attacker solutions and illustrates the importance of this knowledge in the making and evaluating of governmental and public policy decisions.

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Section: Spatial Methodsmentioning

confidence: 98%

Role of Spatial Data in the Protection of Critical Infrastructure and Homeland Defense

Yates

Casas

2010

Appl. Spatial Analysis

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“…The Brazilian native wood market incorporates multiple ventures hold diff erent positions in the production chain, varying in structure and capacity, but dependent of a common natural resource: the wood (Kies et al, 2009;Canova and Hicley, 2012;Köhl et al, 2015;Bösch et al, 2015). In order to maintain a continuous fl ow of wood and other forestderived benefi ts, it is essential to understand market dynamics and nuances shaping relationships between the native wood supply and demand (Syrbe and Walz, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Wood-based industries tend to be clustered on specifi c regions, forming networks and regional production centers (Bösch et al, 2015) that may vary through time and space. Spatial statistics tools can include the spatial component of the native wood market and provide a better understanding about supply and demand, revealing not only changes but also its trends (Kies et al, 2009). In this perspective, this study investigates the spatial and temporal dynamics regarding supply and demand of native wood in Brazil, from 2006 to 2016, considering Minas Gerais State (MG), the second-largest consumer of native wood in the country, as the demand center.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Dynamics of the Brazilian Market for Native Forest Wood: A Case Study for Minas Gerais State, Brazil

Martins

Reis

et al. 2021

Rev. Árvore

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Understanding the Brazilian market for native wood helps forest managers and can guide conservation policies. This study aimed to evaluate the spatial and temporal distribution of supply and demand of the Brazilian native wood for four products namely, charcoal, roundwood, firewood, and sawn wood. We used the consumed wood volume from 2006 to 2016 in Minas Gerais (MG) tracking its supplier in Brazil accounting for 31 million cubic meters. These data were used as input for calculating mean centers and performing grouping analysis. The wood supply centers for the energy sector in Brazil have moved from Cerrado to the Caatinga biome. This change is consistent with historical patterns of agricultural expansion in Brazil. The largest mesoregions producing wood were: East of Mato Grosso do Sul State, South of Goiás State, and Center-South of Bahia State. The solid wood supply centers have moved from the North to Midwest regions but remained within the Amazon biome for almost all the years. This pattern is likely a consequence of timber regulation practices in the Amazon. The mesoregions: Madeira-Guaporé and East of Rondônia State, Southeast and Northeast of Pará State, and North of Mato Grosso State were the most important suppliers of solid wood to MG. Wood consumption for energy is centered in the Center and North regions of MG State, especially in Sete Lagoas, a hub of pig iron and cement industries. Solid wood products are dispersed in MG State, being Belo Horizonte, Conselheiro Lafaiete, Ubá, and Uberlândia the distribution centers of native wood for the furniture industry within MG. The spatial statistics helped to exploit the activity of the native wood market in Brazil and provides essential information for decision-makers that was not previously available.

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“…According to another approach, geographic contiguities of employment, productivity, wages, or population density are used for cluster identification (Cortright, 2006;Desrochers & Sautet, 2004;Ketels & Memedovic, 2008). Alternatively, various indices of spatial association (such as, local Moran's I and Geary's C) can be used for the identification of geographic clusters of economic activities (EAs) (Feser et al, 2001;Kies et al, 2009;Campos & Prophero, 2012). However, these indices do not perform well if geographically referenced information on neighboring localities is sparse or unavailable (Morgenroth, 2008).…”

Section: Introductionmentioning

confidence: 99%

Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe

Rybnikova

Portnov

2015

Lett Spat Resour Sci

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Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying clusters of economic activities (EAs) thus becomes an important step in devising regional development policies, aimed at facilitating regional economic development. However, a major problem with cluster identification stems from limited reporting of specific EAs by individual countries and administrative entities. Even Eurostat, which maintains most advances regional databases, provides data for less than 50% of all regional subdivisions of the 3 rd tier of the Nomenclature of Territorial Units for Statistics (NUTS3). Such poor reporting impedes identification of EA clusters and economic forces behind them. In this study, we test a possibility that missing data on geographic concentrations of EAs can be reconstructed using Light-at-Night (LAN) satellite measurements, and that such reconstructed data can then be used for the identification of EA clusters. As we hypothesize, LAN, captured by satellite sensors, is characterized by different intensity, depending on its sourceproduction facilities, services, etc.,and this information can be used for EA identification. The study was carried out in three stages. First, using nighttime satellite images, we determined what types of EAs can be identified, with a sufficient degree of accuracy, by LAN they emit. Second, we calculated multivariate statistical models, linking EAs concentrations with LAN intensities and several locational and development attributes of NUTS3 regions in Europe. Next, using the obtained statistical models, we restored missing data on EAs across NUTS3 regions in Europe and identified clusters of EAs, using spatial analysis tools. 1 The paper is based on the article prepared for the Special Issue of Letters in Spatial and Resource Sciences on, "Housing Market, Regional Mobility & Economic Growth-Dutch-Israeli Workshop".

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Spatial analysis of regional industrial clusters in the German forest sector

Cited by 19 publications

References 22 publications

Role of Spatial Data in the Protection of Critical Infrastructure and Homeland Defense

Role of Spatial Data in the Protection of Critical Infrastructure and Homeland Defense

Understanding the Dynamics of the Brazilian Market for Native Forest Wood: A Case Study for Minas Gerais State, Brazil

Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe

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