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Gaussian kernel computing. The figure reveals that the first step of computing is to expand the original ROI vector matrix R(a,b) to the size of R(a+2, b+2), where the extended part is filled by the 0 vector. Meanwhile, with the convolution kernel weight corresponding to the 0 vector region set as zeros, the ROI vector in the edge of the original matrix can also be computed.
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The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interes...
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... is the Gaussian kernel where K(m,n) represents the weight of these ROI vectors that are involved for calculating the center ROI R(i,j). With a 3 x 3 Gaussian kernel taken as an example, the specific process is shown in Figure 4. R(a+2, b+2), where the extended part is filled by the 0 vector. ...
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... the parameters (a and b) of the grid division, candidate ROI vectors set R can be represented as the vectors matrix R(a,b) in Algorithm 2. First, lines 1-2 perform the expansion and filling process shown in Figure 4. Next, the convolution multiplication of Equation (8) R'(a,b) will be returned. ...
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... original distribution of the POIs is shown in Figure 13. According to their original distribution, a heat map considering the correlation between them is shown in Figure 14a. As an example, the top-50 query results in size 0.25 km 2 were returned by the RALL method, which is shown in Figure 14b. ...
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... to their original distribution, a heat map considering the correlation between them is shown in Figure 14a. As an example, the top-50 query results in size 0.25 km 2 were returned by the RALL method, which is shown in Figure 14b. The top-50 query results by RALL. ...
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... with Figure 14a, this method was found to be successful in exploring the related ROIs in the map and returning the top-K relevant results based on a correlation meeting the user's query in Figure 14b. It is worth noting that because the vector of each candidate ROI was prefabricated, the multi-keyword query only adjusted the query vector according to the query keyword group, so that the time complexity of the search step was the same as the single-keyword query, i.e., O(n). ...
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... with Figure 14a, this method was found to be successful in exploring the related ROIs in the map and returning the top-K relevant results based on a correlation meeting the user's query in Figure 14b. It is worth noting that because the vector of each candidate ROI was prefabricated, the multi-keyword query only adjusted the query vector according to the query keyword group, so that the time complexity of the search step was the same as the single-keyword query, i.e., O(n). ...
Citations
... Here, we reference a handful of representative examples. The combinations of representations learnt hierarchically include those of individual POIs and users [71], street segments and location trajectories [58], individual POIs and bike share stations [70], POI types and regions [45,72], crime types and regions [47]. ...
Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). In this article, we systematically review the existing research literature in this space to answer the following five research questions. What types of representations were learnt? What SSRL models were used? What downstream problems were the representations used to solve? What machine learning models were used to solve these problems? Finally, does using a learnt representation improve the overall performance?
... Yu et al. (2017) have investigated the relevance of locations to query words. Zhu et al. (2019) address the challenge by focusing on finding the semantic structure of the points. ...
On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the data.
... Thus, every smartphone user is a mobile sensor, reflecting social characteristics and allowing an enormous amount of individual movement data to be collected efficiently in real time. These movement data are used to calculate intercity migration indices [27]. The use of travel-related big data with such high spatiotemporal resolution is more accurate and effective than the use of census data [28]. ...
Large-scale population flow reshapes the economic landscape and is affected by unbalanced urban development. The exploration of migration patterns and their determinants is therefore crucial to reveal unbalanced urban development. However, low-resolution migration datasets and insufficient consideration of interactive differences have limited such exploration. Accordingly, based on 2019 Chinese Spring Festival travel-related big data from the AMAP platform, we used social network analysis (SNA) methods to accurately reveal population flow patterns. Then, with consideration of the spatial heterogeneity of interactive patterns, we used spatially weighted interactive models (SWIMs), which were improved by the incorporation of weightings into the global Poisson gravity model, to efficiently quantify the effect of socioeconomic factors on migration patterns. These SWIMs generated the local characteristics of the interactions and quantified results that were more regionally consistent than those generated by other spatial interaction models. The migration patterns had a spatially vertical structure, with the city development level being highly consistent with the flow intensity; for example, the first-level developments of Beijing, Shanghai, Chengdu, Guangzhou, Shenzhen, and Chongqing occupied a core position. A spatially horizontal structure was also formed, comprising 16 closely related city communities. Moreover, the quantified impact results indicated that migration pattern variation was significantly related to the population, value-added primary and secondary industry, the average wage, foreign capital, pension insurance, and certain aspects of unbalanced urban development. These findings can help policymakers to guide population migration, rationally allocate industrial infrastructure, and balance urban development.
