An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Zhan, Qiwen; Deng, Shanshan; Zheng, Zhen

doi:10.20944/preprints201708.0040.v2

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…(1) T : { (11) T : park, greenland, sight seeing, etc; (12) T : venue, memorial, history, exhibition, museum,etc. ; (13) T : amusement, entertainment, recreation facility, etc. ; (14) T : shop, commerce, mall, supermarket, commodity,etc.…”

Section: A Foundation Of Tourist Interest and Need Label Matrixmentioning

confidence: 99%

“…According to the modeling conditions of interested tourist sight mining algorithm and tour route planning algorithm, the algorithms should combine with motive benefit positive factors . According to the developed algorithm, through the formula (12), formula (13) W in the root node is shown as Figure 8(2). From the figure, the maximum value at the initial element of the maximum heap R and the complete binary tree root node are both 112.260, relating to the tour route growing trees (2) Gt and (21) Gt , that is, the No.2 and No.21 tour route.…”

Section: ) Data Calculation and Obtaining Of The Algorithm Influence Factormentioning

confidence: 99%

“…First, the research on tourists feature attributes and interests are insufficient, and this is the principal condition to meet motive benefit satisfaction. Without thorough research on tourists' feature attributes and interests, optimal tour route cannot be planned [12][13][14][15][16]. Second, the research on typical tourist sights of tourism city is insufficient, in which tourist sight feature attributes and functions to match tourists' interests and needs are not studied and tourist sights are not clustered, which is the second condition to meet motive benefit satisfaction.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the research on typical tourist sights of tourism city is insufficient, in which tourist sight feature attributes and functions to match tourists' interests and needs are not studied and tourist sights are not clustered, which is the second condition to meet motive benefit satisfaction. Without thorough research on tourist sight feature attributes and functions, the precise tourist sights which can meet the needs of tourists will not be mined since the precise tourist sights are the necessary nodes for planning optimal tour routes [17][18][19][20]. Third, there is no research on the data mining of tourist sights based on their precise interest, which is the precondition to plan tour routes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Zhou¹,

Sun

et al. 2020

IEEE Access

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Aimed at current problems in tour route planning, this research proposes a tour route planning algorithm based on the data mining of tourist sights based on their precise interest. It further studies on the aspect of tourist interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and then designs and performs experiment to testify the feasibility and superiority of the algorithm. First, tourist interest and need label matrix and tourist sight feature attribute matrix are set up. Tourist sight attribute factor is formed by mining text encyclopedic data, and combining with other factors, tourist sight clustering algorithm is set up to output tourist sight clusters. Second, data mining algorithm of tourist sights based on their precise interest is set up, which outputs the optimal tourist sights not only match the tourists' interests and needs but also optimally distribute around to produce the lowest travel costs. Third, based on precise interested tourist sights, combining with geographic information data, traffic information data and tourist sight information data, optimal tour route planning algorithm based on interest label is set up. It outputs maximum heap and complete binary tree with descending order interval motive values and corresponding edge clipping circle, which directly reflects the satisfaction content of each tour route on tourists. Tourist sight domain and geographic spatial data of Zhengzhou city are collected as basic data to design the experiment, and it testifies the algorithm's feasibility, effectiveness and practicalness. Meanwhile, the experiment brings in three commonly used route planning algorithms as control group to make comparison with the developed algorithm on the aspect of optimal route, motive value, motive value difference, algorithm performance and guide map,etc., which testifies that the developed algorithm has superiority to traditional algorithms. The research indicates that this developed algorithm solves the problems on tour route planning and it is an effective, feasible and practical algorithm.

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Section: A Foundation Of Tourist Interest and Need Label Matrixmentioning

confidence: 99%

Section: ) Data Calculation and Obtaining Of The Algorithm Influence Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Zhou¹,

Sun

et al. 2020

IEEE Access

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“…Considering all of the features directly or assigning weights to all of them will substantially affect the accuracy and efficiency of the case retrieval process. In this study, the adaptive sweep-circle (ASC) spatial clustering algorithm based on the Gestalt theory, as proposed by Zhan et al (2017), is used to perform the pre-organization of the spatial cases. This algorithm can quickly identify arbitrarily shaped clusters without modifying the initial model (e.g., minimal spanning tree, Delaunay triangulation, or Voronoi diagram).…”

Section: Case Pre-organizationmentioning

confidence: 99%

Spatial case revision in case-based reasoning for risk assessment of geological disasters

Deng

2020

Geomatics, Natural Hazards and Risk

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Case revision has always been challenging in case-based reasoning (CBR) processes. Most CBR methods used for analyzing geological disasters fail to consider the spatial relationships among geological environmental factors. Therefore, conventional case revision rules do not allow for effective case-based reasoning for geologic disaster assessment. In this study, we first establish a spatial case library of historical geological disasters. Subsequently, these spatial cases are organized, reduced, and weighted using spatial clustering, genetic algorithm and rough set hybrid algorithms, respectively. Based on these efforts, we propose a retrieval method for the spatial cases and evaluate the proposed solution for a target case. Finally, a geological disaster assessment model possessing a spatial case revision function is obtained by combining geographic information system (GIS) technology with the genetic algorithm. Experiments and applications show that our spatial revision approach is effective and that it exhibits a higher classification performance compared to traditional data mining methods with regard to geological disaster assessment. The approach also exhibits a higher accuracy and efficiency than typical spatial CBR or case revision models do. The results of this study thus facilitate rapid geological disaster assessment, making it more facile and convenient for decision makers to execute decisions efficiently and quickly.

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A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

Deng,

Liu,

Peng

et al. 2024

IJGI

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Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy.

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An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Cited by 4 publications

References 23 publications

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Spatial case revision in case-based reasoning for risk assessment of geological disasters

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

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