An extended Huff-model for robustly benchmarking and predicting retail network performance

This study proposes a modified Huff model that takes directly into account spatial competition between stores of the same brand, brand attraction based on actual brand performance and spatially variable substitution. The model uses only publicly available or easily acquirable data as input, whereas model output is extensively validated on various levels. These levels include comparison of modeled and real market shares on block, store and brand level for the Belgian food market. Results show that multi-objective optimization of model parameters yields comparable results on block level to other models in the literature but improved results on store and brand levels, thereby ensuring model robustness. This robustness also enables the application of the model for various business purposes as store location determination, leaflet distribution optimization, store and store concept benchmarking, without loss of spatial generality.     

Assessing the impact of retail location on store performance: A comparison of Wal-Mart and Kmart stores in Cincinnati

This study aims to analyze and explain, from a locational perspective, differences in the performance of Wal-Mart and Kmart stores in the Greater Cincinnati Area. A modified Huff model, taking competition and agglomeration into consideration, is developed to estimate the potential sales of individual Kmart and Wal-Mart stores. Results show that the modified Huff model is capable of more accurately estimating individual store sales than traditional interaction models. This study also suggests that the selection of store locations partially explains the difference in performance between Kmart and Wal-Mart stores. The model is calibrated using data collected in 2003, and the validity of the model is supported by the latest development of the stores in Greater Cincinnati Area since 2003.     

The importance of spatial nested data in understanding the relationship between visitor use and landscape impacts

As managers and researchers of protected natural areas continue to seek balance in promoting visitor use but limiting negative experiential and natural resource impacts, the integration of social and physical spatial data may play a critical role in understanding how visitors and the community interface with the landscape within these protected areas. These spatial considerations are important for inventorying, monitoring, and managing the conditions of natural resources within parks. Specifically related to the visitor impacts of natural resources in parks are the use and condition of multiple-use trails. Technology such as GPS and GIS may allow for a unique assessment of the relationship between factors that influence this resource. This paper focuses on how visitor use distribution (measured through GPS tracking), activity type, and trail design influence the impacts to trail conditions. This paper also addresses statistical concerns related to spatial dependency. Results suggest that failure to account for spatial dependency can lead to erroneous Type I findings. Additionally, activity type (specifically horseback riders) and trail design were found to best predict trail impacts when controlling for spatial dependency.     

全球原油贸易网络视角下的亚洲溢价与中日印韩原油潜在竞争关系

随着全球原油贸易关系的复杂化和亚洲原油需求的上升,全球能源市场的“亚洲溢价”现象成为需要关注的议题。本文基于全球石油贸易网络复杂性视角,选取1993—2018年国际原油贸易数据进行样本数据考察,通过构建复杂网络模型和潜在竞争指数,对全球原油贸易网络中的亚洲溢价现象和中日印韩四国潜在竞争力进行分析。结果表明：全球原油贸易集团分化交融,中日印韩呈现出贸易集团化的趋势。从全球原油贸易网络指标来看,中日印韩四国在全球原油贸易网络地位和影响力不断提升,有力推动了全球原油消费中心向亚太地区转移。潜在竞争指数表明,中国与日本、印度、韩国的潜在原油竞争关系呈现出不同特征,中日表现出“先强后弱”的特点,而中印、中韩表现为整体持续增强的特征。