Following the emergence of large numbers of spatiotemporal datasets, the literatures related to spatiotemporal data analysis increase rapidly in recent years. This paper reviews the literatures and practices in spatiotemporal data analysis, and classifies the methods available for spatiotemporal data analysis into seven categories: including geovisualization of spatiotemporal data, time series analysis of spatial statistical indicators, coupling spatial and temporal change indicators, detection of spatiotemporal pattern and abnormality, spatiotemporal interpolation, spatiotemporal regression, spatiotemporal process modelling, and spatiotemporal evolution tree. We summarized the principles, input and output, assumptions and computer software of the methods that would be helpful for users to make a choice from the toolbox in spatiotemporal data analysis. When we handle spatiotemporal big data, spatial sampling appears to be one of the core methods, because (1) information in a big data is often too big to be mastered by human physical brain, so has to be summarized by statistics understandable; (2) the users of Weibo, Twitter, internet, mobile phone, mobile vehicles are neither the total population nor a random sample of the total population, therefore, the big data sample is usually biased from the population, and the bias has to be remedied to make a correct inference; (3) the data quality is usually inconsistent within a big data, so there should be a balance between the variances of inferences made by using data with various quality and by using small but high quality data.

This paper reveals the principle of geographic big data mining and its significance to geographic research. In this paper, big geodata are first categorized into two domains: earth observation big data and human behavior big data. Then, another five attributes except for "5V", including granularity, scope, density, skewness and precision, are summarized regarding big geodata. Based on this, the essence and effect of big geodata mining are uncovered by the following four aspects. First, as the burst of human behavior big data, flow space, where the OD flow is the basic unit instead of the point in traditional space, will become a new presentation form for big geodata. Second, the target of big geodata mining is defined as revealing the spatial pattern and the spatial relationship. Third, spatio-temporal distributions of big geodata can be seen as the overlay of multiple geographic patterns and the patterns may be changed with scale. Fourth, big geodata mining can be viewed as a tool for discovering geographic patterns while the revealed patterns are finally attributed to the outcome of human-land relationship. Big geodata mining methods are categorized into two types in light of mining target, i.e. classification mining and relationship mining. The future research will be facing the following challenges, namely, the aggregation and connection of big geodata, the effective evaluation of mining result and mining "true and useful" knowledge.

The rapid development of information technology has taken us into the "Big Data Era", changed the organization and structure of urban space and residents' behavior, and also caused transformation of the methods in urban temporal and spatial behavior research. On the basis of summarizing the problems of traditional methods such as poor data accuracy, small sample size, weak continuity, and higher costs, this paper first combs through the data acquisition and processing technology for web data mining, residents' behavior data collection and analysis, and network map integration and visual development, which can affect the transformation of the research methods. Then it reviews the latest progress in applying big data to urban temporal and spatial behavior research at home and abroad from the perspectives of residents' behavior, urban space, and urban hierarchy, and builds up a method framework for urban temporal and spatial behavior research based on big data application. The methods in urban temporal and spatial behavior research are going through a great transformation because of the emergence of massive and various information data. Data collection methods have changed from yearbook statistics, social questionnaire survey, in-depth interview to mining of network data (social network data) and application of new spatial position technology (GPS, smart mobile phone, LBS, etc.), and the data shows obviously new characteristics such as large sample size, real-time dynamic, micro and detail, with more attention paid to the extraction of residents' geographic position information. However, as to specific research methods, the traditional ones are still widely used, such as descriptive statistical analysis, cluster analysis, factor analysis, gravity model, network analysis, space-time prism, etc. Generally speaking, the researches of urban temporal and spatial behavior have obvious characteristics of using "new" data and "old" methods to study "newer" and "older" problems at the present stage, and their research scope has also expanded from residential scale to urban space and regional range. However, problems still exist with the current research, such as how to eliminate fictitious data, how to learn and innovate analytical methods, how to expand research field and embody characteristics of the era. Therefore, it is necessary to promote the cross and integration of related disciplines such as sociology, economic geography, cultural geography, tourism geography, computer science, mathematics and geographic information science, in order to find new analysis methods, and also reinforce the research of residents' behavior and urban space by using social network (Twitter, Flikr, Facebook, Sina Microblog, etc.) data or other web (SouFun.com, Dianping.com, Zhaopin.com, Taobao.com, etc.) data, and guide innovation of urban planning methods.

Urban railway systems can reduce environmental footprints by residents' commuting and alleviate traffic congestion in mega-cities. Investigating the characteristics of the spatiotemporal distribution of passenger flows is significant in the examination of traffic demand in public transportation systems. Moreover, the study can help decision makers in traffic demand management. Taking the metro system of Beijing as an example, this study calculated the travel time of over 4 million trips and their origin-destination (OD) matrix. In the investigation of the spatiotemporal patterns, we found that: (1) travel time distribution of all trips and trips during the morning and afternoon peaks well fit with Gamma distribution; (2) patterns of passenger flows between districts or ring roads are symmetric; and (3) spatial inequity has been captured from the evaluation of average transit trips per person per day.

Since 2010, big data has played a significant role in various fields of science, engineering and society. The paper introduces the concepts of geographic big-data, the fourth paradigm and nonlinear complex geographic system, and discusses interactive relationships of these concepts. It is proposed that geographic big-data and the fourth paradigm would become a new opportunity to research on geography complexity. Then the paper discusses how to use the methods of geographic big-data and complexity science to examine geography complexity. For example, based on big-data, a series of indicators of statistical physics fields could be constructed to describe the complex nonlinear characteristics of the real geographic world. Deep learning, complex network and multi-agent methods can be used to model and simulate the complex nonlinear geographic systems. These methods are important for a better understanding of the complexity of geographic phenomena and processes, as well as the analysis, simulation, inversion and prediction of complex geographic systems. Finally, the paper highlights that the combination of geographic big-data and complexity science would be the mainstream scientific method of geography in the 21st century.

China has been experiencing rapid urbanization at an unprecedented rate and as a result, urban internal space structure has evolved significantly. It is of great significance to label different functional regions (DFR) inside a city for urban structure analysis, policy making, and resource allocation. These DFRs include residential district, industrial district, education district, and the administration district. This paper explored the characteristics and distribution of urban functional regions based on big geographic data. With the latest road network data, the study area (i.e., 6 districts of Guangzhou city in Guangdong Province, China) was partitioned into 439 segments. By applying the employment of spatial and temporal semantic mining method to the one-week massive floating cars GPS data and the point of interest data, we developed a Latent Dirichlet Allocation (LDA) and Dirichlet Multinomial Regression (DMR) model. Moreover, OPTICS clustering method was employed to process the results of LDA and DMR to identify different functional zones. Meanwhile, status map of Guangzhou urban planning, and resident travel characteristics were used to verify the verification of mentioned results. The results show that this method can identify the obvious characteristics of urban functional areas, such as mature residential area, science and education culture area, commercial area, and development zone. The results also show that residential and commercial areas are dominant DFRs in Guangzhou city, which are surrounded by other types of functional regions. This paper brings a new perspective on using large-scale and high quality individual space-time data to study human migration and daily activities, as well as to explore social space to unveil the formation and mechanism of urban functional zones.

Urban signs characterize the state of development and operation of a city, including construction conditions of built environment,driving force of urban economic and social development, operational status of facilities and urban activities of individuals in the city, etc. The diagnosis of urban signs equals to the health examination of urban development and operation, by which sticking points are recognized. A set of reliable and practical urban diagnostic indices is required not only to comprehensively reflect correlative sub urban systems that are static or dynamic, but also illustrate the status of urban system through quantitative methods and geo-visualization. Using traditional data and big data from different sources, this paper constructs a system of diagnostic index of urban signs based upon the integration of urban activity-travel system, urban population system, urban operation system, and urban environment system. The diagnostic index system is decomposed into 4 dimensions including fundamental force, driving force, pressure and vitality. The fundamental force index is used to describe basic attributes of land use and population; the driving force index reflects the state of development of spatial units through development of enterprises and quality of the environment; the pressure index is used to monitor the running status of the urban system, and as such, it plays a role in risk-evaluation and risk-warning; the vitality index reflects the real vitality of the spatial units by demonstrating the dynamic characteristics of the activity system and flows in time and space. 12 spatio-temporal scales are acquired through intersection of 4 levels of the spatial units（municipal Shanghai , district, Jiedao, census tract）and 3 levels of temporal scales（annual,daily and real time levels）. The index weight is determined by fuzzy hierarchy analysis. Taking April 6, 2016 as an example, we calculate both comprehensive and dimensional diagnostic index of urban signs of Jiedaos (subdistrict that is sub-divided into several residential communities or neighbourhoods) in Shanghai and elaborate on how the diagnostic index of urban signs corresponds to actual state and facilitates detection of urban problems. Results show that comprehensive diagnostic index varies slightly while considerable variations emerge in diagnostic index of each dimension. Fundamental force index, driving force index and vitality index decline gradually from inner city to suburbs. On the contrary, pressure index increases from inner city to suburbs. Through visual and real-time analysis and evaluation, the diagnostic index of urban signs has huge potential for implementation in urban grid management, pressure warning and other needs of urban governance.

The rising of POI (Point of Interest) data drives an innovation of urban research. In order to sort out the progress of urban research using POI data in China, summarize the directions of research, methods of data analysis and shortcomings, and provide references for the future application of POI data in China’s urban development, CiteSpace was used to analyze 625 related literatures in CNKI (China National Knowledge Infrastructure) database from 2010 to 2019. Result shows that a large number of literatures in urban research using POI data emerged since 2013, and boomed in 2017. According to the results of knowledge map analysis, the main applications of POI data in urban research are identification of urban functional areas, division of urban central areas and boundaries, identification of business agglomeration and recommendation of interested points. While the main methods for analyzing POI data are kernel density analysis, spatial correlation analysis and DBSCAN algorithm. Plenty researches show that POI data is a kind of effective data for urban research, and very helpful for researchers to better understand the spatial structures, distribution patterns and development rules of cities. In future, it can be combined with machine learning and the other algorithms to provide a decision-making method for a long-term development of urban expansion and internal functional structure adjustment. However, POI data can not replace the shape data in some scenarios, and the impact of public awareness should be seriously taken into account individually for different researches.

Data visualization is an important service in remote sensing applications. To address the problems that it is difficult for the static pre-built map tile service to meet the requirements of professional data view, map configuration, spatial analysis and other applications, this paper presented a solution architecture for the real-time rendering and interactive visualization of remote sensing big data. Firstly, on the rendering nodes, a rendering-tile structure for image was constructed to improve the reading speed of remote sensing images. Secondly, on the visualization servers, a data-computing load balancing strategy was proposed to optimize the rendering efficiency of map tiles. Thirdly, a set of service interfaces for the interactive visualization was designed for the front ends of services. Compared with the traditional technology, this solution can not only achieve the real-time rendering and the interactive visualization of remote sensing data, but also obtain an equivalent service performance to the pre-built tile map service. Finally, based on the above solutions, an interactive visualization prototype system of remote sensing data was developed and was applied into the demonstrations of the real-time viewing of remote sensing images, the visualized computing and the visualized analysis.

In the internet era, "Big data" wave spread rapidly to the economic and social fields. Geography is the natural laboratory in which big data research and application can be seen at work. The written speech focused on collision between geography and big data. It reviewed big data research and application in geography study. We also discussed the opportunities and challenges we would face during this collision. In summary, big data has had a certain influence on the geography research, especially in the human geography domain. Geographic information science will develop rapidly in the internet era of big data. But there are few disturbances in physical geography. Big data can not change the core proposition and the basic paradigm of geography. We should hold an open inclusive attitude to big data theory study and application research in geography.

The rapid development of information and communication technologies and the wide use of smart devices in people′s daily life have generated a large amount of data-the big data. The information exploration has not only greatly influenced our life, work and thinking, but also presented a new paradigm of social science research, that is, from data-scarce to data-rich study, from static to dynamic analysis, from relatively simple hypothesis and model to comprehensive simulations and theory. Especially these big data with spatial information brings opportunities for human geography. Big data meets geography’s quantitative revolution and social transition and should not be overlooked, since it provides a large amount of timely and detailed data of human behavior. In the meantime, geographers also face risks and challenges. Firstly, geographers require careful rethinking with respect to the philosophy of science and consideration on the important distinction between data and knowledge. Data never speak for themselves and require contextual knowledge with respect to analysis and interpretation. Secondly, it is hard to say that geographers have well underprepared for big data. Geographers have long been used to the traditional statistical methods which are designed with regard to data-scarce science. New methods of handling and analyzing data sets that consist of millions or billions of observations are urgently needed. Thirdly, despite the emerging data deluge, there are a number of ethical and security challenges in working with these data. How to define the scale and boundary of data is also a challenge, since the data are growing all the time. Then this paper points out that emphasis should be equally placed on quantitative and qualitative research and small-data and big-data research. Geographers should pay attention to the "digital divide" since access to big data is limited, and geographers should cooperate with scholars in other academic fields.

Understanding human mobility patterns and spatial structure is of great important to urban planning, traffic management, emergence response, and so on. With the development of information and communication technologies, it is possible to collect large-scale, long-term human tracking data, which brings great opportunities and challenges for human mobility behavior studies. This article first introduces the main datasets being used for studying human mobility patterns, then reviews the recent progress from the perspectives of human travel behavior and urban spatial structure respectively. We found that most studies follow the route of "data-human travel behavior-urban spatial structure," mining and understanding human mobility patterns from the datasets and further giving insights on the characteristics of urban spatial structures. However, there exist few studies on the influence of urban spatial structure on human travel behavior. In the future, it is necessary to integrate multi-source spatiotemporal data to understand the interaction between human mobility and urban spatial structure, develop spatiotemporal analysis theory and models for dealing with mobile location big data, and focus on understanding the coupling relationship between human mobility and urban spatial structure.

The technology of "big data" has profoundly changed our life and society, and advanced scientific research. By taking social and human activities as main data source, this technology is of great potential of applications in human and economic geography. Drawing on recent progress in research, this article analyzes the new applications of big data to the research of urban hotspots, functional areas and boundaries, transportation and consumption behaviors and social geography. Based on these analyses, this article articulates the roles of big data in enriching data sources, adding new research themes, bringing new research paradigms, and stimulating the research of coupling to human-spatial research in human and economic geography. However, the technology of "big data" still needs improved, especially the "bias" issue in collection and the attributes of data. It also needs appropriately positioned in the application in human and economic geography because big data cannot replace the data that are collected from field work, or be applied without proper theoretical grounding and hypothesis, and replace the independent thinking of researchers and decision processes. These factors limit the application of big data, which requires more efforts on big data infrastructure development as well as exploration of human and economic geography. Acknowledging the opportunities and roles of big data application, human and economic geographers should emphasize the following to advance the research of this filed: exploring new data sources and paying closer attention to database construction inhuman and economic geography, establishing a research paradigm towards big data applications, facilitating cross-disciplinary and cross-domain research to strengthen the study of human-nature relations, and emphasizing the research towards human behaviors and demands.

Spatial differentiation of natural and human factors in the land surface system of the Earth is the main concern of Geography. Given the complexity of the land surface system, different research methods should be applied to different issues concerning the system. Based on past geographical research, four paradigms were generalized, including geographical empirical paradigm, geographical positivist paradigm, geographical system science paradigm, and geographical big data paradigm. Appropriate paradigms should be employed for different scientific questions, and multiple paradigms should be applied to some complicated questions.

The ability to acquire the remote sensing data is increasing day by day, which directly causes the remote sensing data to become diverse and massive, and the issue that the massive amount of data is being non-affordable to store has become more and more prominent. On the other hand, due to the lack of an effective and efficient method of storage management, the data that theterminal application need is difficult to found in a timely manner, therefore, is stored but useless. This paper focuses on the storage and management problems of the massive, high through put and spatially structured remote sensing data and the basic land information products. We have presented a storage and management method which uses the big data structure and can integrate both the vector and raster data. Based on the MongoDB database, the prototype system is realized and we use the data of PB rangeto test it. Eventually, we have proved that this method meets the demand for the storage and management of the remote sensing vector-raster data in the era of big data. On the basis of the study results and prototype system, the following studies need to be further explored: (1) The organization and management methods for internal data of resources, especially the objective and timely management for the vector data; (2) Real-time interactive visualization methods for different data types and storage modes of resources, achieving dynamic extraction and rendering ability based on in the heterogeneous data model; (3) To construct large data computing architecture on the heterogeneous type storage mode, and to implement multimodal computing framework to meet the needs of the remote sensing applications require.

Polycentricity has become the spatial development strategy in many Chinese cities. Effective identification of polycentric urban form is of great significance for assessing planning performance and formulating better planning strategy. Compared with Baidu Heat Map and cell phone call data, Tencent Easygo data have the advantages of high spatial and temporal resolution and low acquisition cost, which can be used to compare the population agglomeration capabilities of different urban centers, and dynamically identify and monitor polycentric urban form. Taking Chongqing metropolitan area as an example, this study applied the method of Kernel density analysis to identify its polycentric urban form and the size and rank of the urban centers based on Tencent Easygo data of one week. The results show that Chongqing as a mountainous city has developed a polycentric urban structure with multiple subcenters and urban clusters due to the constraint of natural environment, the drive of economic forces, and the guidance of urban planning. The main center and several subcenters inside the inner ring road have comparable degrees of population aggregation, land use scales, and urban development levels. These subcenters are also stronger than the other subcenters outside the inner ring road. The study pointed out that the urban development degree and population aggregation capacity of Xiyong, Chayuan, and peripheral urban clusters need to be improved.

As a complement for urban public transportation, taxi plays an increasingly important role in people’s life, which is also taken as one of the most important windows opening to the public. With the rapid development of economy, traffic condition is becoming more and more terrible, which causes the heavy traffic situation in many cities in China. Taxi is a type of transportation resourece that is dynamic and unbalanced in different road networks and at different time, which faces a lot of problems, such as the difficulty in finding a taxi for a passenger or finding a passenger for a taxi driver. This makes taxi transportation to be poorly efficient, and negatively affects the performance of government. It is helpful to learn about the dynamic space in the city, and the patterns of citizens’ working, living and travelling after studying the features and rules of taxis’ pick-up and drop-off distribution. Moreover, it is helpful to learn about the “hotspots” in the city, which represents the areas with huge volumes of taxis’ pick-up and drop-off activities. Based on taxis’s GPS trajecotries big data, this paper puts forward a new model called Line Density Model (LDM) to detect the space time distribution pattern of taxis’pick-up and drop-off activities, in which there are linear trends existing within the taxis’ pick-up and drop-off, and the “hotspots” exhibiting linear trends near the road network in the city. Finally, Wuhan city is taken as the testing area, and the experimental result shows that taxis’ pick-up and drop-off distribution is unbalanced in different areas and at different time, which helps to understand the dynamic and the pattern of the public’s working, living and travelling, and gives a reference to find the “hotspots” at different time in Wuhan city.

With the rapid development of the Internet, Internet of things, and cloud computing technology, data with geographical location and time tag are accumulated in an explosive way, and this indicates that we are in the era of big spatiotemporal data. In addition to the typical "4V" characteristics, big spatiotemporal data also contain rich semantic information and dynamic spatiotemporal patterns. Although massive spatiotemporal data have promoted the evolvement of various cross-disciplinary studies, traditional methods of data processing and analysis would no longer meet the requirements of efficient storage and real-time analysis of such data. Therefore, it is of great importance to integrate big spatiotemporal data with high-performance computing/cloud computing. To address this problem, this article begins with the concept and origin of big spatiotemporal data, and introduces its unique characteristics. Then, the performance requirements generated by current big data applications are analyzed, and the status quo of the underlying hardware and software is summarized. Furthermore, the article comprehensively reviews parallel processing, analysis, and mining methods for big spatiotemporal data. Finally, we conclude with the challenges and opportunities of storage, management, and parallel processing analysis of big spatiotemporal data.

In the era of big data, the rapid growth of geographic spatial temporal data has challenged the conventional application concepts, technical framework and service modes. In this paper, the concept and features of geographic spatial temporal big data is elaborated firstly. Then, the characteristics and challenges of the geographic spatial temporal big data computation are analyzed. Particularly, the theory of collaborative computing and service for the geographic spatial temporal big data is developed, which includes four levels of collaboration: data collaboration, technology collaboration, service collaboration and producing collaboration. According to the demand of the market-oriented operation and platform-based service, the technical frameworks of the geographic spatial temporal big data collaborative computing are designed. Furthermore, four common key technologies are discussed, including the remote sensing data preprocessing, the geographic spatial temporal data storage and management, the high performance computing and the visualization of geographic spatial temporal big data. Next, the remote sensing data processing system is developed, and is taken as a case to illustrate the implementation of collaborative computing and service of geographic spatial temporal big data. At last, this paper forecasts the future application mode of geographic spatial temporal big data.

Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

This article researched into the pig’s circulation in China by using data visualization and combined analysis of the current status of pig’s circulation and the Chinese urban agglomeration. At the end of the study, the results showed the spatial distribution pattern of pigs in China based on the status quo of the development of urban agglomeration, and provided the basis for policy formulation for the relevant agricultural sector. In this article, Beijing, Tianjin, Shanghai, Chongqing, Heilongjiang, Jilin, Liaoning, Shandong, Hebei, Henan, Hubei, Hunan, Anhui, Jiangsu, Zhejiang, Sichuan, Yunnan, Guangdong, Guangxi and municipalities were chosen as the research object. Firstly, statistical data on pigs was integrated with GIS data, remote sensing data and network data to result in an integrated multi-source data. When big data technology and GIS was used to visualize this integrated multi-source data we can get the status quo of pig’s circulation in 19 provinces and municipalities. The visualization process was based on the New national urbanization plan (2014-2020) and the results of previous studies for dividing China into urban agglomeration. The results of the study was due to the combined analysis of the status quo of the pig’s circulation and the visualization results of the urban agglomeration in china. The study summed up the current situation of the pigs circulation of the city group to get the spatial distribution pattern of pigs based on the development of urban agglomeration in China. Finally, the problems of the distribution pattern of pigs in China was analyzed to give the corresponding solutions. According to the analysis of the spatial distribution of pigs, the research object was divided into 4 categories: The first category showed that the region needs to be supplied with a large number of pigs, including Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong; The second category showed that, the region have both pigs inflow and outflow, including the three northeastern provinces, Henan, Shandong, Hubei, Hunan, Sichuan; The third category showed that, live pigs move out of the region including Hebei, Anhui, Yunnan, Guangxi; The fourth category is Chongqing, there is not a lot of pigs inflow and outflow. Beijing, Tianjin city group, the Yellow River Delta city group and the Zhujiang River Delta city group is the most important pork consumption areas, 69.6% of the pigs flow is related to the three groups. The main pigs production areas are concentrated in the middle, the east, the southwest and the northeast of China, and the east and southeast coastal areas are the main pigs consumption—it can be summarized as ‘pigs of the west supply in the east, pigs of the north supply in the south’. The eastern coastal areas are the most important part of the pig’s circulation system in China, the production and consumption of the pigs in the eastern coastal areas is the key factor for the change of the pig’s circulation pattern in China.

The contemporary studies of tourism big data are not sufficient to utilize online tourist-generated contents for evaluating tourism destinations, while the content-analysis studies in linguistic studies have yet to have qualified technics for conducting tourism research. In order to bridge this gap between these, this paper constructs an emotion model for evaluating tourism destinations based on tourists' online reviews. This model is composed of three emotional filtering factors including tourism lexicon, grammatical logics and emotional multipliers. The tourism lexicon contains 3507 positive emotional words and 3365 negative emotional words. It is used to depict the general emotional image of a tourist online review by calculating positive and negative words within the review. Every emotional word will be counted as one score, either positive or negative. Grammatical logics contain 13 rules which adjust the positive or negative scores and give the final emotional score of the review. Emotional multiplier in this study is set from three to five. It is used to correct the deviation of exaggerated positive emotions due to the existing pro-positive preference in human emotional expression. This paper collects 120731 pieces of tourists' online reviews among eight tourist destinations (Yangshuo, Zhangjiajie, Huangshan, Chengdu, Luoyang, Kanas, Jiaozuo and Xishuangbanna) and uses this model to evaluate the overall emotional images of these destinations. The result is compared to the questionnaire-based survey data conducted by UNWTO (United Nation, World Tourism Organization) among these destinations from 2013 to 2015. The verification proves that the three emotional filtering factors are effective in mapping emotional images of tourists' online reviews. Based on both single and multiple-year verification, the accuracy of the proposed six sub-models is ranged from high to low as follows: C2>C1>C3>B>Direct Scores>A. This outcome means that the model-based result is the closest to the UNWTO result under the C2 that emotional multiplier is set at 4 and the tourism lexicon and grammatical logics are applied. This paper contributes to the literature by paving alternative ways of destination evaluation and proves the usefulness of tourism big data in geographical studies. This effort will underpin subsequent theoretical and empirical studies in tourism geography.