Store image has been measured frequently by means of structured scales. Some researchers exhort against the use of structured scales for the measurement of this construct and recommend the use of unstructured measures instead. They argue that structured scales are inadequate for capturing the “gestalt” associated with the perception of a store image. This research attempts, for the first time, to investigate empirically the relative efficacy of the structured scales and the unstructured measures of store image. The results reveal that the two types of measures have similar properties and that the structured scales are more correlated with a set of self-reported behavioral measures. Thus practitioners should feel more comfortable utilizing structured, semantic differential scales to assess their store image.

Alternative Modes of Measuring Store Image: An Empirical Assessment of Structured versus Unstructured Measures

How Maps Work

Bidimensional regression is a method for comparing the degree of resemblance between 2 planar configurations of points and, more generally, for assessing the nature of the geometry (Euclidean and non-Euclidean) between 2-dimensional independent and dependent variables. For example, it can assess the similarity between location estimates from different tasks or participant groups, measure the fidelity between cognitive maps and actual locations, and provide parameters for psychological process models. The authors detail the formal similarity between uni- and bidimensional regression, provide computational methods and a new index of spatial distortion, outline the advantages of bidimensional regression over other techniques, and provide guidelines for its use. The authors conclude by describing substantive areas in psychology for which the method would be appropriate and uniquely illuminating.

/pdf/bidimensional-regression-assessing-the-configural-similarity-3nroecif1e.pdf

Bidimensional regression: assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets.

The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city’s operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens’ data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.

https://www.mdpi.com/2220-9964/5/2/16/pdf

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.

https://www.mdpi.com/2071-1050/11/12/3392/pdf

Destination Image Analytics Through Traveller-Generated Content

Spatial thinking in geographic information science: a review of past studies and prospects for the future

This paper examined spatial descriptions for guiding Japanese and American tourists from cross-cultural and geographic perspectives, based on a content analysis of 24 guidebooks to four cities in Japan and USA. Quantitative analysis of pictorial and linguistic information in guidebooks revealed that Japanese guidebooks use predominately pictorial information, whereas American guidebooks mainly depend on the linguistic one. In addition, we found a complementary relationship between the two modes of information. The contents of linguistic information were entirely influenced by socio-cultural factors rather than environmental conditions such as the street pattern regularity. In particular, difference in address systems between two countries affected the way of sorting the sites, style of maps, and the use frequency of linguistic information.

Cultural differences of spatial descriptions in tourist guidebooks

https://tokyo-metro-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=2912&item_no=1&attribute_id=18&file_no=1

Yoshiki Wakabayashi

Papers

Spatial thinking in geographic information science: a review of past studies and prospects for the future

Cultural differences of spatial descriptions in tourist guidebooks

Spatial analysis of cognitive maps

Spatial Patterns of Population Change in the 23 Special Wards of Tokyo after the Period of the Bubble Economy

Role of geographic knowledge and spatial abilities in map reading process: implications for geospatial thinking