Strategic alliances in a town centre : Stakeholder’s perceptions of property owner’s role

In order for town centres to manage increased competition in retailing, co-operation between stakeholders in a strategic alliance has become more important. A typical set of stakeholders in a strategic alliance for strengthening retailingare retailers, local authorities and property owners. The roles of retailer’sand local authorities’ are well researched. However, the role of property owners is not. The aim of this paper seeks to unfold the role of property owners in a strategic alliance. This is a case study of a medium-sized town in which semi-structured interviews with different stakeholders were conducted. In the chosen town there is a TCM alliance co-operation at work. The above mentioned stakeholders are possible members in an alliance. The case studied shows a fragmented property owner market with no dominant property owner, as it is in many medium-sized towns. Our study shows that many stakeholders look at the role of property owners as crucial for town centre development. However, property owners do not see that they can significantly contribute to or benefit from the development.The main reasons for this opinion are that they consider themselves as not having enough resources or the capability to influence the town development.

Out-of-town shopping and its induced CO2-emissions

Planning policies in several European countries have aimed at hindering the expansion of out-of-town shopping centers. One argument for this is concern for the increase in transport and a resulting increase in environmental externalities such as CO2-emissions. This concern is weakly founded in science as few studies have attempted to measure CO2-emissions of shopping trips as a function of the location of the shopping centers. In this paper we conduct a counter-factual analysis comparing downtown, edge-of-town and out-of-town shopping. In this comparison we use GPS to track 250 consumers over a time-span of two months in a Swedish region. The GPS-data enters the Oguchi’s formula to obtain shopping trip-specific CO2-emissions. We find that consumers’ out-of-town shopping would generate an excess of 60 per cent CO2-emissions whereas downtown and edge-of-town shopping centers are comparable.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

USGS Topographic Maps over a Hillshaded Digital Elevation Model

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