Impact of garment industries on road safety in metropolitan Dhaka

There are about 4,000 garment industries in Bangladesh, most of them are clustered in and around the capital city. Together they account for 75 percent of the country's export earnings and employ around 1.8 million people which is almost one half of the total industrial workforce of the country. Though it is the most important economy sector of Bangladesh, unplanned and haphazardly built garment factories are also inducing many social, housing and most importantly urban transportation problems which are a great cause of concern. This study investigates the impact of garment industries on transportation, in particular road safety issues of garment workers. Data is collected to identify the locational problems of garment factories, spatial distribution of worker residences, and their travel pattern as well as to assess their walking and road crossing problems. Finally, recommendations are put forward to tackle transport problems arising from these unplanned establishments of export oriented garments industries in Dhaka Metropolitan City

Combinatorial locational analysis of public services in metropolitan areas. Case study in the city of Volos, Greece.

Social prosperity largely depends on spatial structure, a relation which becomes stronger in urban areas where the quality of life is menaced by several factors. Traffic, over-building, lack of open space and deficient location of services come to the fore. The latter reflects access inequality and is one of the main reasons for everyday movement difficulties of citizens. Particularly, public services, as part of the public sector, are considered to be driven by the principle of social well-fare. Therefore the study of their location gives rise to the question: how can access of city blocks to public services be evaluated and how can the results of this evaluation be combined with the monetary values assigned by the state? In this respect, the main aim of this paper is the determination of a synthetic methodological framework for the locational analysis and evaluation of public services in urban areas. The proposed approach is based on spatial analysis methods and techniques as well as on the analytical capabilities of GIS and finally leads to the definition of the locational value for each city block. The public services are classified according to served population age groups and to their yearly utilization levels. The minimum and average Manhattan distances to the services of each classification group are calculated along with the percentages of services that are closer than a critical radius to each city block. At the final step, city blocks are classified through the use of cluster analysis to the calculated distances and percentages and then ranked according to their overall accessibility to public services. Their score is utilized in the definition of their locational value and in the formulation of a combinatorial index which compares locational and land values throughout the study area. The methodological framework is applied in the city of Volos where according to the results of the analytical process the majority of city blocks (60,7%) indicates a comparatively lower locational than monetary land value.

Finding Multiple New Optimal Locations in a Road Network

We study the problem of optimal location querying for location based services in road networks, which aims to find locations for new servers or facilities. The existing optimal solutions on this problem consider only the cases with one new server. When two or more new servers are to be set up, the problem with minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some useful properties about the potential locations for the new servers, from which we derive a novel algorithm for MinMax, and show that it is efficient when the number of new servers is small. When the number of new servers is large, we propose an efficient 3-approximate algorithm. We verify with experiments on real road networks that our solutions are effective and attains significantly better result quality compared to the existing greedy algorithms

Assessing clustering methods for exploratory spatial data analysis

Exploratory spatial data analysis continues to be an important area of research. The use and application of clustering methods for the analysis of spatially referenced data is beginning to show some promise. However, a variety of clustering methods does exist. It is essential that a better understanding of these approaches in the geographic domain be pursued in terms of data requirements, computational efficiencies and inherent biases. This paper presents an initial attempt to demonstrate strengths and weaknesses of various clustering approaches for exploratory spatial data analysis.