Health-oriented optimization strategies of coastal fitness environment in Qingdao

Under the initiative of “co-construction and sharing, health for all”, the natural condition of coastal area in Qingdao becomes the key area of the national fitness. Based on the health-oriented theory, authors analyze the health-oriented connotation of the coastal fitness environment, and suggest that the realization of health-oriented coastal fitness environment needs to meet the spatial demand of health-oriented behaviors and health behaviors. These behaviors should maintain accessibility and friendliness. Based on the present status survey and IPA analysis, it is concluded that the continuity and gradual changes are in urgent need of improvement, followed by smoothness, then convenience and recognizability need to update. Therefore, in combination with the acceptable time and distance, authors put forward specific optimization strategies from four aspects. That is, coastal fitness environment in Qingdao can be improved by network of coastal footpaths, intelligent management of color lightings, reasonable connecting of road materials and humanized and recognizable fitness site, in order to promote residents’ health activities and national fitness.

Attentional Modulations of Alpha Power Are Sensitive to the Task-relevance of Auditory Spatial Information

The topographical distribution of oscillatory power in the alpha band is known to vary depending on the current focus of spatial attention. Here, we investigated to what extend univariate and multivariate measures of post-stimulus alpha power are sensitive to the required spatial specificity of a task. To this end, we varied the perceptual load and the spatial demand in an auditory search paradigm. A centrally presented sound at the beginning of each trial indicated the to-be-localized target sound. This spatially unspecific pre-cue was followed by a sound array, containing either two (low perceptual load) or four (high perceptual load) simultaneously presented lateralized sound stimuli. In separate task blocks, participants were instructed either to report whether the target was located on the left or the right side of the sound array (low spatial demand) or to indicate the exact target location (high spatial demand). Univariate alpha lateralization magnitude was neither affected by perceptual load nor by spatial demand. However, an analysis of onset latencies revealed that alpha lateralization emerged earlier in low (vs. high) perceptual load trials as well as in low (vs. high) spatial demand trials. Moreover, across all conditions, participants with earlier alpha lateralization onset showed faster response times. Finally, we trained a classifier to decode the specific target location based on the multivariate alpha power scalp topography. A comparison of decoding accuracy in the low and high spatial demand conditions suggests that the amount of spatial information present in the scalp distribution of alpha-band power increases as the task demands a higher degree of spatial specificity. Altogether, the results offer new insights into how the dynamic adaption of alpha-band oscillations in response to changing task demands is associated with post-stimulus attentional processing.

Location Based Business Recommendation Using Spatial Demand

Business locations is most important factor to consider before starting a business because the best location attracts more number of people. With the help of web search engines, the customers can search the nearest business location before visiting the business. For example, if a customer need to buy some jewel, he makes use of search engines to find the nearest jewellery shop. If some entrepreneur wants to start a new jewellery shop, he needs to find a best area where there is no jewellery shop nearby and there are more customers in need of jewel. In this paper, we propose an algorithm to find the best place to start a business where there is high demand and no (or very few supply). We measure the quality of recommendation in terms of average service time, customer-business ratio of our new algorithm by implementing in benchmark datasets and the results prove that our algorithm is more efficient than the existing kNN algorithm.

Locating Station of One-Way Carsharing Based on Spatial Demand Characteristics

This study aims to explore factors that affect carsharing demand characteristics in different time periods based on EVCARD transaction data, which is the largest station-based one-way carsharing program in Shanghai, China. Monthly usage intensity and degree of usage imbalance are used as proxies of demand. This study uses three groups of independent variables: carsharing station attributes, built environment (density, diversity, design, and destination accessibility), and transportation facilities. The adaptive elastic net regression is developed to identify factors that influence carsharing usage intensity and degree of usage imbalance after factor selection using extra-randomized-tree algorithm. Finally, a station layout is proposed according to both usage intensity and degree of imbalance. The main results of this study are presented as follows: (1) different effects of built environment and transportation factors cause dynamic demand across different time periods; (2) factors with positive and negative effect on carsharing demand are divided clearly for guidance of the carsharing station layout; (3) public parking space leads to more personal vehicle trip compared to a carsharing trip; and (4) as public transportation, the relationship of the metro and carsharing is complementary. However, the bus stop and carsharing have a competitive relationship. This study provides a carsharing layout method based on both usage intensity and degree of imbalance. Furthermore, several policies concerning carsharing are proposed.