Simulating spatial market share patterns for impacts analysis of large-scale shopping centers on downtown revitalization

金沢大学理工研究域環境デザイン学系The decline of the downtown has been observed in many cities across the world. In response, many small cities in Japan, for example, have been making regeneration efforts including development controls on large-scale shopping centers. It is extremely useful to analyze the potential effects of relevant planning policies before implementation. We developed an urban planning support tool, a multiagent simulation (MAS) model called Shopsim-MAS, to investigate the impacts of some downtown revitalization policies through consequent spatial dynamics of shop market shares. We discuss methods to model household behavior and to understand the market area dynamics of shops. The Shopsim-MAS model developed in this project has proven to be a useful means to analyze the impact of downtown revitalization policies in Japan. It is also expected to be further expanded for impact analysis of similar or more sophisticated urban policies in other parts of the world. © 2011 Pion Ltd and its Licensors

Is there a paradigm shift for GIS data representation and analysis?

Abstract. In the era of big data, and particularly location-based big data, GIScience is facing significant challenges. The traditional data representational and analytical models have been primarily limited to the view of Newtonian space and time. However, the contemporary enormous amount of location-based social media data and other forms of voluntary geographical data has greatly enhanced the potential to expand the horizon of the field of GIScience by including data that represent more aspects of human activities in the world. For instance, human interactions and information exchange are taking place not only in the physical space but other in virtual spaces, or concurrently in both types of spaces. Similarly, locations may not only exist in the physical space but also in virtual spaces. Social connections may also be traced in either physical or social spaces, or both. Is the shift of ways people interact with each other and with the real world imposing fundamental changes in physical activities in the physical space? If so, how? Ultimately, how can GIS help to organize the data in order to answer new research questions?This abstract is developed in response to the call for submissions to the research agenda session organized by the commission on geospatial analysis and modeling. Among other important and interesting research directions, I choose to discuss the following topics. I will provide my partial assessment of the current state of knowledge as well as preliminary analysis of associated research questions.Revamping the representation framework of current GISNew representational framework is needed to organize data in spatial, social, and temporal space. Wei and Yao (2018) argued that current GIS representations do not distinguish between spatial location and virtual locations in the virtual space, neither do they account for social associations among people. They proposed an ontological framework that identifies four primary categories in the location-based social media data, namely Agents, Activities, Places, and Social Connections. Such framework is an example of what need to be represented and analysed in future GIS.Representational bias of current location-based social media dataIt is widely known that the demography of social media users is not representative of the demography of the general public. However, the location-based social media data are used anyway in many studies regardless of the representative bias. Little has been done to understand the nature of the bias and how the bias impact research findings. There is a dire need for research that can shed light on a better understanding of the bias and on possible responses to the problem.Data fusionIn the era of big data, with a myriad of data sources and data types, how to integrate the heterogeneous data is a challenge task. Yao et al (2019) suggested that developing analytical data fusion approaches is an important research direction for location-based big data.Analytical models for spatio-temporal-social relationshipsTraditional GIS analysis and modelling focuses on space and spatial relationships, while sometimes the temporal dimension is also added. However, location-based big data are often acquired from individuals with fine-grained location and time information. Location-based social media data show connections among the individuals. In other words, social connections are embedded in such spatially and temporally informed data. Therefore, it is possible and highly beneficial to explore data in the integrated social-spatial-temporal dimensions. Traditional models were not developed for the high dimensional dynamics. New analytical models are in great demand to analyse the data to discover patterns and relationships in social-temporal-social dimensions. </jats:p

Assessing Place Type Similarities Based on Functional Signatures Extracted from Social Media Data

Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some level of similarities. However, previous studies have paid little attention to the place type similarities. This study proposes an analytical approach to measuring similarities between place types in multiple typing schemes based on functional signatures extracted from web-harvested place descriptions. In this study, a functional signature consists of three component signature factors: place affordance, events, and key-descriptors. The proposed approach has been tested in a case study using Twitter data. The case study finds high similarity scores between some pairs of types and summarizes the situations when high similarities could occur. The research makes two innovative contributions: First, it proposes a new analytical approach to measuring place type similarities. Second, it demonstrates the potential and benefits of using location-based social media data to better understand places

Assessing Place Type Similarities Based on Functional Signatures Extracted from Social Media Data

Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some level of similarities. However, previous studies have paid little attention to the place type similarities. This study proposes an analytical approach to measuring similarities between place types in multiple typing schemes based on functional signatures extracted from web-harvested place descriptions. In this study, a functional signature consists of three component signature factors: place affordance, events, and key-descriptors. The proposed approach has been tested in a case study using Twitter data. The case study finds high similarity scores between some pairs of types and summarizes the situations when high similarities could occur. The research makes two innovative contributions: First, it proposes a new analytical approach to measuring place type similarities. Second, it demonstrates the potential and benefits of using location-based social media data to better understand places.</jats:p

Location-based services and GIS in perspective

This paper examines location-based services (LBS) from a broad perspective involving deWnitions, characteristics, and application prospects. We present an overview of LBS modeling regarding users, locations, contexts and data. The LBS modeling endeavors are cross-examined with a research agenda of geographic information science. Some core research themes are brieXy speculated

Spatial queries with qualitative locations in spatial information systems

We discuss locations as defined by their qualitative spatial relations to other features, dubbed qualitative locations (QL). We further propose a mechanism to handle queries with qualitative locations in geospatial information systems. For the realization of the mechanism for QL-based queries, we propose a conceptual framework that takes advantage of models of qualitative spatial reasoning to bridge the gap between conventional metric spatial information systems and the general publicÕs common-sense query of spatial relations in natural language. Ó 2004 Published by Elsevier Ltd

Simulating spatial market share patterns for impacts analysis of large-scale shopping centers on downtown revitalization

The decline of the downtown has been observed in many cities across the world. In response, many small cities in Japan, for example, have been making regeneration efforts including development controls on large-scale shopping centers. It is extremely useful to analyze the potential effects of relevant planning policies before implementation. We developed an urban planning support tool, a multiagent simulation (MAS) model called Shopsim-MAS, to investigate the impacts of some downtown revitalization policies through consequent spatial dynamics of shop market shares. We discuss methods to model household behavior and to understand the market area dynamics of shops. The Shopsim-MAS model developed in this project has proven to be a useful means to analyze the impact of downtown revitalization policies in Japan. It is also expected to be further expanded for impact analysis of similar or more sophisticated urban policies in other parts of the world.