The study of spatial and temporal characteristics of human behavior has been widely noted among different disciplines such as geography, physics, planning and epidemiology. By integrating spatial and temporal dimensions, the space-time GIS provides a fundamental approach for the analysis and exploration of human behavior characteristics. However, the existing space-time GIS is not strong in some aspects, including representation of human behavior process in space and time as well as interactive analysis of human behavior in space-time contexts. This paper proposes a space-time GIS approach for human behavior studies, by bringing some core concepts of time geography such as "diorama" and "project", into space-time GIS, and discusses the potential and challenges from three aspects, including dynamic representation, correlation and variation perception of geographic entity in space and time.
With the combination and development of geographic information technology and computer network technology, geographic information services based on global framework demand for more efficient massive data management, the traditional single center relational database management mode is unable to meet the requirements. Since the distributed file systems, semi-structured databases and relational database technology have complementary advantages to each other, a new technical method for efficient management of massive data is developed. In order to achieve high effective geospatial data management, this paper presented an integrated architecture oriented to the storage and management of geospatial data in distributed environment, designed user - oriented massive geospatial data integration model and distributed storage organization model . In this model, a technical route combining the NoSQL database and relational database is adopted, and a layered + partitioned data model and multi-level index mechanism for the rapid access of massive data is designed, so it can realizes the integrated management of vector and raster data in distributed environment. Because the model has taken advantages of relational database and semi-structured database, structured geographic information, spatial index and entity data can be managed separately and the efficiency of data processing and access is improved effectively. Vector data and raster data is the largest and most widely used geospatial data, In this paper, an experiment system is set up in the experiment environment, which realizes vector data and raster data management model. TB-level data are used to conduct experiments of data loading, index (pyramid) creation and concurrent data access efficiency, compared with the traditional data model, the model in the data management capacity, processing speed and access efficiency have greatly improved. The results show that the model can support the parallel operation in distributed environment, with a higher data management capability will offer an effective solution to massive data management in distributed environment.
：Urban rail transit stations are the key nodes of the basic urban rail transit network system. The scientific classification of the rail transit stations is significant to understand the urban functional zoning and evaluate the construction of the rail transit infrastructure. The time series data of urban rail transit stations objectively records the important information of observed stations at all-time points. The time series data contains different patterns, which reflect different sequence genesis. Therefore, studying cluster of the time series data is an important means to recognize and understand the essence of time series data formation. It is also a major method to mine higher value of principle and knowledge that implied in time series data. In this paper, we use smart card data of urban rail transit stations in Beijing, and divide the big data into four data sets: weekdays boarding data set (WB), weekdays alighting data set (WA), weekends (rest day) boarding data set (RB) and weekends alighting data set (RA) to describe characteristics of each station’s daily passenger volume. Symbolic Aggregate approXimation (SAX) is firstly introduced to analyze four data sets, which effectively reduces the dimensionality of high-dimensional data and realizes similarity measure between stations. Finally, it is more reasonable to classify the 195 rail transit stations into 8 types according to the DB index by hierarchical clustering method. They are residential stations, work stations, partial residential-based residential and work mixed stations, dislocation stations, tourist attractions and commercial stations, partial work-based residential and work mixed stations, integrated stations and other stations. The performance of SAX is compared with Euclidean distance similarity measure. The results indicate that SAX outperforms Euclidean distance in terms of accuracy and efficiency. The paper analyzes characteristics of daily passenger boarding and alighting volume on four data sets and spatial distribution of each type. It is found that residence and dislocation stations are mostly located in the far end of the subway, while the types of work stations, tourist attractions and commercial stations, partial work-based residential and work mixed stations, and integrated stations are concentrated in the urban areas. Partial residential-based residential and work mixed stations scatter around the city center. The results can help to interpret the different functional zoning of the city and the characteristics of residents' travel behavior, which provides a basis for understanding the urban spatial pattern and its evolution process, and also provides some objective reference for planning, design and management services of rail transit stations.
：Urban rainstorm waterlogging is one of the urban' s most common natural disaster. Since the environment of urban underlying surface is complex, urban rainstorm waterlogging has a short response time and submerged speed characteristics. Digital Elevation Model (DEM) is the key of urban surface water catchment, and it is also the basis of rainstorm waterlogging inundation analysis, but the DEM building by traditional methods cannot meet the needs of urban flood analysis under complex underlying surface environment. This paper based on the thinking of multi-source data fusion terrain correction, aiming at constructing the urban DEM for the surface water catchment analysis. This paper used multi-source data fusion approach to incorporate DEM obtained by digital photogrammetry method and technology, then, modified the fused DEM using building、water system elements which change the route of urban runoff. This paper selected the Gongzhufen area in Haidian District, Beijing, to verify the feasibility of this method, the result showed that the urban DEM with a relatively high accuracy, especially in the urban road area where the accuracy was at the centimeter level. The DEM realized the key expression of urban road area which is easy to accumulate water, moreover, the water network and catchment area extraction results based on it were more accurate. The DEM built by this method was suitable for urban rainstorm waterlogging analysis.
Bacillary dysentery is a common disease as well as a public health problem with much attention. In recent years, the incidence of bacterial dysentery is rather prevalent in Beijing-Tianjin-Tangshan region. This paper analyzed the seasonal and population characteristics of bacillary dysentery in Beijing-Tianjin-Tangshan region in 2012 firstly. Then, we explored the spatial and temporal clustering of the incidence of bacillary dysentery by using hotspot analysis model. We also investigated the quantitative relationship between the incidence of bacterial dysentery and the social-economic factors by using geographical detector model. The results showed that: (1) the peak attack time of bacillary dysentery was August. The age range that had the highest incidence was 0-9 years old, followed by those above 80 years old. The population that had the highest incidence was farmers, followed by the scattered children. (2) The incidence of bacterial dysentery clustered in both space and time in Beijing-Tianjin-Tangshan region. In space, the high clustering regions for incidence of bacillary dysentery are mainly located in Fangshan District and Mentougou District of Beijing and Binhai New Area of Tianjin;the low clustering regions are mainly located in Luan county of Tangshan. In time, the disease occurred in all the 12 months in 2012 in the high clustering regions, but mainly occurred in January, February, March, April and June in the low clustering regions. (3) The major socio-economic factors affecting the spatial distribution of incidence of bacterial dysentery included the proportion of rural population, population density and per capita GDP of each district or county, which explanatory power was 61%, 37% and 20%, respectively. The interactive effects were stronger than their individual effects. This study analyzed the population characteristics, spatial and temporal characteristics and influencing factors of incidence of bacillary dysentery in Beijing-Tianjin-Tangshan region and provided a theoretical basis for the prevention and control of bacterial dysentery in these regions.
The assessment factors described in the existing landslide susceptibility studies can be cataloged into the aspects of meteorology, hydrology, topography, geology, vegetation, human activities, and others. These conditioning factors are derived from different sources and are hard to collect completely, especially for the ungauged area. As an important data source for the assessment of landslide susceptibility, DEM is easy to obtain. Therefore, the purpose of this study is to assess the landslide susceptibility using the DEM data and its derived factors only. In this study, The assessment factors were divided into two datasets. The first dataset was derived from DEM, which contains eight landslide conditioning factors, including: altitude, slope, aspect, topographic relief, curvature, stream power index (SPI), sediment transport index (STI) and topographic wetness index (TWI). The second dataset, which is used as the comparison group, was gathered by using the same conditioning factors of the first dataset, but with the addition of some other conditioning factors, including: vegetation coverage, land use, soil type, and average annual precipitation. Based on the above two groups of conditioning factors, the logistic regression model and the weights-of-evidence method are employed to assess the landslide susceptibility in Dehua county of Fujian province in China. The prediction rates of the landslide susceptibility results were 73% and 83% by using the factors of the first dataset and the second dataset, respectively. As a result, the DEM-derived conditioning factors were more efficient in generating an accurate landslide susceptibility map. The conclusions made in this study can be used as a reference for the assessment of landslide susceptibility in the ungauged area.
Considering the normalization characteristic of drought in Yunnan Province during recent years, it’s very significant to study the drought in Yunnan province. Nowadays, major studies focus on the meteorological drought in Yunnan Province and the studies focus on agricultural drought in Yunnan Province are few. But the data used in the study methods of meteorological drought can’t represent vegetation growth state. Also they cannot be used to evaluate the effect of drought on vegetation or analyze temporal and spatial distribution of agricultural drought in Yunnan Province. Because of these disadvantages, this paper calculated the vegetation condition index(VCI) of Yunnan Province during 2004-2013, identified agriculture drought events and analyzed temporal and spatial distribution of agricultural drought. Before the temporal and spatial distribution was analyzed, agricultural drought events identified by VCI were compared with meteorological drought events identified by the standardized precipitation evapotranspiration index (SPEI) and the Pearson correlation coefficient was calculated between VCI and precipitation to evaluate the capability of VCI index.We found the similarities and differences between these two types of drought events and analyzed the possible reasons. The results revealed that there were differences between drought events identified by these two indices because drought events identified by SPEI index are based on meteorological elements such as precipitation and temperature while drought events identified by VCI index are based on vegetation growth state which is not only affected by meteorological elements. The low Pearson correction coefficient also demonstrates precipitation is just one of the key factors which affect vegetation growth state. Though there are differences between these two types of drought events, VCI and SPEI can both monitor drought and identify classical drought events well. Based on this conclusion, temporal and spatial distribution characteristics of agricultural drought in Yunnan Province were analyzed using drought frequency index and drought-area-ration index. The results showed that: drought frequency of spring and winter is higher than that of autumn and the drought frequency of summer is the lowest. The spatial distribution of drought frequency during spring, summer and winter is relatively uniform while the drought frequency of northern Yunnan during autumn is higher than that of southern Yunnan. Overall, the drought frequency of northern Yunnan is higher than southern Yunnan and drought-area-ratio of Yunnan during 2004-2013 shows a decreasing-increasing and fluctuating trend. Drought-area-ratio index of spring and winter is the highest whose values are 46.63% and 47.18%, respectively. Both of them show a decreasing trend. Drought-area-ratio index of summer is the lowest whose value is 43.81% and shows an increasing trend. The value of drought-area-ratio index of autumn is between spring and summer and has a decreasing tendency. Based on these results, agricultural drought of spring and winter is most prone to happen and the extent is the maximum while the agricultural drought of summer is least prone to happen and the extent is the minimum. Since vegetation growth state is not only affected by drought but also can be affected by plant diseases and insect pests, irrigation, frozen injury and improper fertilization, this study monitoring agricultural drought based on VCI index has some limitations. Future work should focus on the physical mechanism of agricultural drought and the biophysical response of vegetation to drought in order to monitor and forecast agricultural drought more accurately.
Hand, foot, and mouth disease (HFMD) is a common infectious disease, particularly for the 5 years aged and younger children. In recent years, there is a substantial increase in HFMD cases in China, and the outbreak of this disease has attracted the public and health authorities. Although many studies focus on the variation pattern of HFMD, only a few explored thoroughly on its variability at the space-time dimension and its determinants. Here we used a Bayesian spatio-temporal model to analyze the county-level data of HFMD in Shandong Province from May to August in 2008. The study cases of human disease were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were collected from the publicly available Chinese Meteorological Data Sharing Service System (
Analyzing factors that affecting the spatial distribution of aerosol can help researchers to understand the changing mechanism of aerosol, which provides a scientific reference for regulating the atmospheric quality. In this research, taking Xiamen city as a case study, the MODIS -Aqua and Landsat8 OLI images were used in the aerosol optical depth (AOD) inversion and land cover classification, respectively. Then, the impacts of forests and built-up areas on the spatial distribution of aerosol were compared by employing the correlation analysis, the simple linear regression model and the variation partitioning. It is concluded that: (1) the combination of Dark Dense Vegetation (DDV) algorithm and the interpolation method was appropriate for the computation of AOD inversion during the spring season in Xiamen; (2) the AOD for the built-up areas was significantly higher than that for the forests; and (3) the forests had more impacts on the spatial distribution of aerosol than the built-up areas. Results of this study have significances and referential values for the improvement of urban atmospheric quality and ecological environment.
Global warming is one of the most daunting challenges that our humanity is facing. It has urged the action to study carbon emissions and carbon sequestration ability to control the regional climate change. Some studies show that human activities, especially the combustion of fossil fuel, cause the carbon emissions and the global climate warming. Therefore, it is important to work on energy saving and emission reduction. At the same time, the carbon sink capacity of forest, grassland, crops and other vegetations become one of the most powerful approaches to ease the global climate change. Thus, we conduct a research in this area, in order to improve the carbon source utilization efficiency, reduce the carbon intensity, prefect the energy-saving and emission-reduction works, and well manage the carbon budget capacity and some other problems. Taking Youjiang district of Baise city in Guangxi Province as the experimental area, this article uses the object-oriented classification technology and extracts the area geographic information from the Landsat 8 OLI and Google Earth images. Due to the complex terrain of the study area, different parameter settings of the multi-scale segmentation are used and the optimal scaling for the image segmentation is selected. We also use the membership function method, the closest classification method and the CART decision tree classifier method to complete the object-oriented classification layer by layer and evaluate the accuracy of the classification results, based on the spectral difference, geometric shapes, objects, texture and other characteristics. Through summarizing the conversion relationship between the land and carbon coefficient, combining with the high-precision object-oriented classification results, the estimation model of carbon budget capacity is built based on land-cover types. Finally, according to the CASA model of carbon budget capacity, we check the accuracy of the estimation method. The carbon budget capacity of Youjiang district is estimated to be -3996.4 kt, according to the coefficient that corresponds to the feature carbon conversion relationship. Integrated with the administrative planning, population distribution, DEM, and other relevant data, the carbon budget capacity of Youjiang district is analyzed thematically. The results showed that: (1) the use of RS and GIS technologies in the studies of regional carbon budget capacity reveals a distinct advantage. The multiscale segmentation and object classification method can efficiently eliminate the extraction error caused by spectral confusion, and solve problems such as the large quantity of spatial data faced by the traditional classification, the classification of "salt and pepper", the exact utilization of different classification methods, and the improvement of the classification accuracy of carbon. (2) We summarized the findings of the coefficients for carbon balance capacity from domestic and international researches, and applied it in the construction of carbon balance estimation model. The object-oriented method and carbon balance estimation model were used to interpret the land cover data to estimate the Youjiang area carbon balance capability. Results show that the high degree of forest land, grassland, cultivated land and other types of vegetation areas is responsible for the performance of carbon sink in the study area. At the same time the construction land consumes a lot of fossil fuel to play as a large source of carbon. But the overall carbon sink ability of Youjiang area is stronger than its carbon emissions. It is conducive to the stable development of the regional ecological system, and may ease the regional threat of climate change. (3) Combined with the administrative divisions of Youjiang district and DEM data, the spatial analysis was carried out. We summed up the overall characteristics of the carbon budget capacity in the Youjiang district. The carbon budget is large in quantity in the central districts. Moreover, studies have shown that the elevation, slope aspect and human activities could affect the carbon budget capacity. It specifically demonstrated that the areas of high-altitude, steep slopes and less human activity reveal a large amount of carbon sink and less carbon footprint. On the contrary, the areas of low-altitude, lower slopes and more human activity reveal a large carbon footprint and lower amount of carbon.
Remote sensing based urban surface energy information sufficiently reveals the interactive relationship between urban surface energy and land surface element types, which objectively reflect the structures and variations of the high surface energy areas, the open urban fields and the open urban space networks. To attribute the changing urban thermal environment to different combinations of land surface element types, the study area is divided into 154 grids. Seattle of USA, one of the international livable cities, is included as a reference city to further investigate how urban surface energy responses to different processes of urbanization. The following results are noted: (1) planning/designing the high surface energy areas and open urban fields with a better arrangement of land surface element types could benefit the balance of urban surface energy at both the grid scale and the regional scale; (2) for the massively expanded urban hard landscape, the vertical volume of the construction entities is more sensitive to the urban surface energy concentration and change, with respect to their horizontal distribution and group combination; (3) the percentage of open fields in grids will be higher than the high surface energy areas, when the impact of different surface element types on urban thermal environment shows an equality with the same absolute contribution index; (4) an significant decrease in urban surface energy will be found when forest and water occupies more than 20% of the grids; (5) contrarily, a remarkable increase in urban surface energy will appear when the under construction/industrial surface element types accounted for more than 5% of the grids, or when the proportion of high density construction land has reached up to 30% of the grids. In general, this study considers the spatial relationship between urban surface energy and land surface element types and thus to provide scientific references for urban planning and design.
"Heat island effect" is one of the main features of modern urban climate. In this paper, we have obtained the information for regions with different building densities in Beijing by using the artificial visual interpretation based on the high resolution remote sensing images, and then analyzed the relationship between the urban building density distribution and the urban heat island effect and its change pattern based on the land surface temperature data obtained by remote sensing inversion. The results show that the medium density building regions are the primary type within the fifth ring road in Beijing, and the area proportion of which is 23.5%. The distribution of the high density building regions is slightly less than the medium density building regions, and the area proportion of which is 12.01%. There are evident differences among the distributions of regions with different building densities within different ring roads. The high density building regions are mainly distributed within the second ring road, while the medium density building regions are commonly distributed in the whole area. The medium density building regions and the low density building regions are mainly distributed within the second-third ring road area. The overall area of the high-rise building regions is very small, and the high-rise building regions are mainly distributed within the second-third ring road area as well as the third-fourth ring road area. The relationship between the land surface temperature and the building density for the urban building regions is significantly positive, in which the higher density of the urban buildings, the higher average land surface temperature it will reach. The average temperature of the high density building regions in Beijing reached 30.5 ℃. The contribution of the high-rise building regions to the heat island intensity is small, and the average temperature of the high-rise building area is 28.32 ℃, which is 2.18 ℃ lower than the high density building regions. The distribution pattern of the average temperature for regions with different building densities among different ring roads is approximately the same. The differences of the average temperature among regions with different building densities within the second ring road are the smallest, while the average temperature of the high density building regions is obviously higher than other building density regions within the second-third ring road area and the third-fourth ring road area, and the average temperature of the high-rise building regions within the fourth-fifth ringroad area is the lowest, which is 28.09 ℃. Taking the change of heat island intensity between 2010 and 2015 into consideration, only the heat island intensity in the high-rise building regions has a weakening tendency, in which the intensity of the heat island has reduced by 0.07 ℃. While the heat island intensity in the high, medium and low density building regions have an enhancing tendency, the heat island intensity in the high density regionshas the largest growth, which had an increase of 0.56 ℃. Heat island effect is one of the most representative ecological environment problems in the process of urbanization, and the intensity of urban construction has an important impact on the urban heat island effect. Base on many researches, an appropriate reduction of the urban building density can effectively ease the occurrence of urban heat island effect.
Based on the MODIS NDVI data for the Three-River Headwater Region from 2000 to 2013, this study first systematically analyzed the spatial-temporal change pattern of yearly accumulative NDVI during the growing season. Then, the impacts of human activities on the change of NDVI were explored based on the statistical data collected in the ecological protection and construction projects implemented within the study area. Finally, correlations between the NDVI data and climate factors were acquired, and the key limiting climate factors that could impact the vegetation change within different parts of the study area were discussed. Results show that, the accumulative NDVI during the growing season increased overall from 2000 to 2013. Specifically, areas with apparent increase of NDVI accounted for 17.84% of the study area and were mainly distributed in the western and northern parts, while areas with apparent decrease of NDVI covered only 0.78% of the Three-River Headwater Region and were sporadically located in the central part. NDVI in the eastern and southern parts primarily presented a stable change trend or had no significant change trend, and its area ratio was 59.64%. Although the ecological protection and construction projects implemented within the Three-River Headwater Region had promoted the vegetation restoration, NDVI change was mainly affected by the climate factors within the whole study area, since the impacts of human activities on the regional vegetation change were limited. Spatially, NDVI in the western part of the study area (Yangtze River source region) was strongly affected by temperature, while in the northeastern part (Yellow River source region) it was affected more by precipitation. In the southern part (Lancang River source region), however, vegetation growth was affected neither by precipitation nor by temperature.
Chronic exposure to fine particulate matter with the aerodynamic diameter less than 2.5 μm (PM2.5) is a major risk factor for cardiovascular and respiratory morbidity. Environmental problems of PM2.5 in China have aroused great concerns within both the government and the public. However, studies of this subject have been limited due to the spare distribution of national PM2.5 monitoring network. Satellite-derived aerosol optical depth (AOD) measurements can provide the spatiotemporally resolved monitoring and have been widely applied to estimate the ground level PM2.5 concentration. So far, the AOD measurements only have moderate spatial resolutions (3 to 17.6 km) which are insufficient for resolving the city level aerosol features. In order to address this issue, a 1 km resolution AOD retrieval algorithm-SARA (for MODIS instruments) was applied. Additionally, based on the high-spatial resolution fusion images, the PM2.5 related factors (such as the land use data, dusty area and major roads information) had been extracted. This study then proposed a novel geostatistical modeling method that integrates the fine resolution retrieved AOD and the high resolution area-source emission data to investigate the spatial variability of PM2.5 levels. Results show that the SARA retrieved AOD can obtain an average correlation coefficient R of 0.99 and an RMSE of 0.08, when comparing with AERONET measurements, which indicates that SARA has good accuracy and reliability in representing the aerosol conditions over the study area. Results show that, the annual average value of PM2.5 even exceeded 75μg/m3 and a discrepant spatial heterogeneity occurred between seasons. The worst conditions occurred in the winter, and then in the autumn. The GWR-predicted PM2.5 concentrations (with an average R2 of 0.66) are strongly correlated with the in situ observations. The spatial patterns revealed in the mapping results show that the high-level PM2.5 pollution clustered in southern parts of the study domain, while the low-level mainly appears in the rural or mountain land within the northern areas. Furthermore, these findings recommend the combination superiority of GWR model and multi-source remote sensing data, as well as introduced the great potential of fine-scale PM2.5 simulation in distinguishing the spatial patterns of PM2.5 and guiding the air pollution control strategies.
