Methodological issues in applying Location Models to Rural areas

Location Models are usedfor planning the location of multiple service centers in order to serve a geographicallydistributed population. A cornerstone of such models is the measure of distancebetween the service center and a set of demand points, viz, the location of thepopulation (customers, pupils, patients and so on). Theoretical as well asempirical evidence support the current practice of using the Euclidian distancein metropolitan areas. In this paper, we argue and provide empirical evidencethat such a measure is misleading once the Location Models are applied to ruralareas with heterogeneous transport networks. This paper stems from the problemof finding an optimal allocation of a pre-specified number of hospitals in alarge Swedish region with a low population density. We conclude that the Euclidianand the network distances based on a homogenous network (equal travel costs inthe whole network) give approximately the same optimums. However networkdistances calculated from a heterogeneous network (different travel costs indifferent parts of the network) give widely different optimums when the numberof hospitals increases.  In terms ofaccessibility we find that the recent closure of hospitals and the in-optimallocation of the remaining ones has increased the average travel distance by 75%for the population. Finally, aggregation the population misplaces the hospitalsby on average 10 km.

Distance measure and the p-median problem in rural areas

The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances with the Euclidean in a rural region witha sparse, heterogeneous network and a non-symmetric distribution of thepopulation. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (2-100 facilities). We find that the network distance give as gooda solution as the travel-time network. The Euclidean distance gives solutions some 2-7 per cent worse than the network distances, and the solutions deteriorate with increasing P. Our conclusions extend to intra-urban location problems.

A framework for ICT-based knowledge sharing in sustainable rural development : the case of Gilgit-Baltistan, Pakistan

Some 50% of the people in the world live in rural areas, often under harsh conditions and in poverty. The need for knowledge of how to improve living conditions is well documented. In response to this need, new knowledge of how to improve living conditions in rural areas and elsewhere is continuously being developed by researchers and practitioners around the world. People in rural areas, in particular, would certainly benefit from being able to share relevant knowledge with each other, as well as with stakeholders (e.g. researchers) and other organizations (e.g. NGOs). Central to knowledge management is the idea of knowledge sharing. This study is based on the assumption that knowledge management can support sustainable development in rural and remote regions. It aims to present a framework for knowledge management in sustainable rural development, and an inventory of existing frameworks for that. The study is interpretive, with interviews as the primary source for the inventory of stakeholders, knowledge categories and Information and Communications Technology (ICT) infrastructure. For the inventory of frameworks, a literature study was carried out. The result is a categorization of the stakeholders who act as producers and beneficiaries of explicit and indigenous development knowledge. Stakeholders are local government, local population, academia, NGOs, civil society and donor agencies. Furthermore, the study presents a categorization of the development knowledge produced by the stakeholders together with specifications for the existing ICT infrastructure. Rural development categories found are research, funding, agriculture, ICT, gender, institutional development, local infrastructure development, and marketing & enterprise. Finally, a compiled framework is presented, and it is based on ten existing frameworks for rural development that were found in the literature study, and the empirical findings of the Gilgit-Baltistan case. Our proposed framework is divided in four levels where level one consists of the identified stakeholders, level two consists of rural development categories, level three of the knowledge management system and level four of sustainable rural development based on the levels below. In the proposed framework we claim that the sustainability of rural development can be achieved through a knowledge society in which knowledge of the rural development process is shared among all relevant stakeholders.

How does the use of different road networks effect the optimal location of facilities in rural areas?

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.