Since the economic reform and opening up, Guangdong Province has been one of the provinces in China that has the largest floating population inflow. Data from the sixth census show that Guangdong had 21497787 inflow population from other provinces, which accounted for 20.61% of the total population in Guangdong in 2010. The number of inflow population in Guangdong ranked the first in all provinces, autonomous regions, and municipalities in China. Using data from the fifth and sixth national population census in 2000 and 2010 and spatial autocorrelation method (global autocorrelation, local autocorrelation, and cold and hot spot analyses), we analyzed the spatial- temporal characteristics and influencing factors of inflow population in Guangdong Province during the first decade of the 21st century. The results show that: (1) From 2000 to 2010, the number of inflow population in Guangdong increased sharply, but the distribution pattern of the inflow population was stable. The concentration of the inflow population slightly decreased from 2000 to 2010. (2) The number of inflow population in Guangdong Province was closely related to economic development, but the distribution pattern had a clear relationship with local industrial transfer policies.

In order to model the optimal discretization of site-river distance of prehistoric settlements and to obtain a quantitative characterization of the correlation between sites and river, this paper takes Lushi County of Henan Province as an example and uses the method of geographical detector for analysis. The model is to discrete the continuous geographic data based on the values. Based on this analysis, the paper discusses the performance of four classification methods (Equal Interval—EI, Quantile Value—QV, Natural Break—NB, and Geometrical Interval—GI) in the model for the Peiligang period, the Early Yangshao period, the Late Yangshao period, and the Longshan period. It then analyzes the structure, development pattern, distribution, and scope of the settlements for a better understanding of the human-environment relationship in prehistoric settlements from the perspective of societal organization and development state and cultural and behavioral patterns of humans in prehistoric time. The results show that: (1) Optimal discretization of site-river distance is realized using the classification method of NB, QV, NB, and GI with class number of 8, 8, 8, and 6 for the four periods. The power of this determinant for determining the density of sites is 39.5%, 70.8%, 73.0%, 59.8%; (2) Floods caused the terrace on both sides of the river to collapse gradually and reduced the area of the terrace. In order to gain more living space within the limited area, the sites dispersed along the river. When the terrace area became too small, dispersion along the river was too costly and the ancient settlements began to expand away from the river. Therefore the determining power of the factor of site-river distance first increased and then decreased. (3) In terms of improvement strength, NB/EI>GI>QV; in terms of improvement efficiency, EI/GI>NB>QV; in terms of the power of the determinant, GI>QV/NB>EI; (4) Settlement structure changed from simple, sparse, and loosely structured in the Peiligang period to polarized in the Early Yangshao period, then developed into a stage composed of three segments in the Late Yangshao period. The driving force of settlement development changed from population growth in the early stage to structural change of the society. Settlement distribution and human activities concentrated within 1~2.5 hours walking distance from the river and continued to expand. This is the result of waterborne disease aversion and reflects the ample supply of labor and food resources brought by the optimized division of labor in the society and possibly the invention of new technologies and tools.

With the development and application of Information Technology and Geographic Information System in the past decades, the traditional approach in the research on space and land use changes has changed from statistical analysis to dynamical simulation and forecasting. As policy prescription has increasingly favoured a planning approach to regulate and control urban spatial changes, decision-making support for urban and rural planning has attracted a great focus from scholars in this area. Research approaches in this field, in a vast and varied range, include spatial analysis, urban modeling, geosimulation, virtual reality, etc. In this article, based on the review of research progress around the world in this field and the discussion on future research trends, a new concept, namely, service resources, in planning support systems for urban and rural planning, was introduced. The service resources in planning support were divided into three categories: data as a service (DaaS), application programming interfaces as a service (APIaaS), and Platform as a Service (PaaS). In order to explain this new concept well, the researches on planning support systems (PSSs) worldwide were reviewed in the three aspects. Many case studies and researches in this area, from the development of GIS to employment of cloud computing for urban simulation, were also reviewed, and the review helped further discuss how the service resources are integrated into the researches to change PSSs development. Detailed information about the synthesis of DaaS and APIaaS with PaaS for planning support was introduced and emphasized as well. The development of PSSs has gone through the stages from statistical analysis to presentation by 2 dimension GIS or remote sensing data, to dynamical simulation by combining GIS or remote sensing data in simulation models. As for the development of Virtual Reality (VR), VR technology began to be used to visualize and present urban planning and design schemes, which allowed PSSs turn into 3 dimension visualization and modeling. In the meantime, the concept of collaborative planning was gradually replacing traditional planning concept, and the researches on PSSs also started to focus on this topic. GIS dataset, simulation models and web service began to be integrated into one platform, made possible by the development of cloud computing. Thus, the development of DaaS, APIaaS and PaaS will be a new trend and also a necessity for the researches on the support for urban and rural planning. This paper includes 4 sections. Section 1 introduces the background of the current work. The concept and classification of service resources for planning support of urban and rural planning is introduced in section 2, as well as a detailed review on current development regarding to these service resources. Then, how to develop PSSs by integration of the DaaS, APIaaS and PaaS is discussed in section 3. Last, in section 4 the current status of the development of the integration is reviewed and the future developmental trend in this research area is also discussed to help researchers to do more standardized and systematized researches in the future.

The information of spatial distribution of population plays a significant role in the studies on resource environment, social economics, evaluation of the loss caused by a natural disaster, land use change, and other topics in geography and related disciplines. Traditional method hypothesizes that a population is distributed uniformly in a region, but the actual situation is not like that. Spatialization of census data for a population becomes rather important for a comprehensive analysis which combines social economics with natural environment. Therefore, the research on spatialization of census data has become a hot spot in geographic science and other social sciences. In this paper, Taips County, a typical region in the agro-pastoral zone of North China, was taken as a study case. This region has been heavily affected by the Grain-for-Green Project in China, and the population in the region has changed dramatically since the implementation of the project. Based on the characteristics of each of the 175 administrative villages in the region in 2000 and 2008, different scales were applied in the analysis. Through multi-variables regression analysis of the population's census data and various impacting factors, including land use indexes, topographical indices (mean elevations and mean slopes), and distance to main roads and rivers at the village's level in Taips County, using GIS software and SPSS statistical software as the tools, a model for the spatial distribution of population was established. In the meantime, the actual population density of each administrative village was used to validate the precision of the model. In this study, the number of independent variables was gradually increased to explore the model of the population's spatialization to achieve higher precision and make the model more suitable to the study area. It was found that there was a significant correlation between population density and land use type of each administrative village. The correlation ratio between actual administrative village's population density and the density calculated by spatialization model reached to 0.961 and 0.881 in 2000 and 2008, respectively, and the linear fitting slopes of both simulation results were close to 1. These results indicated that the spatialization model worked very well for simulation, and the accuracy satisfied the application of the model to the research on population's spatial distribution in small scales. And also, dividing spatial scales can improve the precision. In addition, the population in the region has changed dramatically during the 8 years. The population grew rapidly near the town center and in the suburban areas but dropped sharply in the other areas, indicating the trend that the population became concentrated near the town center and the surrounding areas. In conclusion, Grain-for-Green Project is one of the most important driving factors of the change of the spatial pattern of population in the regional scale.

Readily available and accurate data on spatial population distribution is essential for understanding, and responding to, many social, political, economical and environmental issues, such as humanitarian relief, disaster response planning, environment impact assessment, and development assistance. Research on spatialization of demographic data plays an important role in grid transformation of social-economical data. Furthermore, as gridded population data can be effectively interoperate with geospatial data and remote sensing images, it is a useful supplement to census data. This paper reviewed spatialization methodologies, predictive modeling factors and typical datasets in the literature of population data spatialization research. Shortcomings of demographic data and advantages of spatial population distribution data are compared and summarized firstly. The spatialization methodologies are grouped into three categories, i.e., population distribution models from urban geography, areal interpolation methods and spatialization methods based on remote sensing and GIS. Population models from urban geography include the Clark's model and allometric growth model. The areal interpolation methods had been distinguished by point based method and area based method. Spatialization methods based on remote sensing and GIS are most widely used in nowadays, which can be further grouped into three categories for two reasons: one is the relationship between population and land use, urban area, traffic network, settlement density, image pixel characteristics, or other physical or socioeconomic characteristics, and the other is the calculation strategy. Various methods mentioned above have their own application environment and limitations. We reviewed the principles and applicability of every method in detail. After that, we generalized the frequently used factors in the spatialization process, involving land use/land cover, traffic network, topography, settlements density, night light, texture variable, and spectral reflectance. In the meantime, some typical research cases about the factors also were exemplified and analyzed. In addition, we introduced a few widely used spatial population distribution datasets or influential population spatialization projects. They consisted of China km grid population datasets, UNEP/GRID, GPW/GRUMP, LandScan, AfriPop & AsiaPop & AmriPop. The producers, resolution, characterization year and generation method of each one were presented exhaustively. Based on the above review, we discussed the current research problems and outlined research priorities in the future. The problems include the temporal inconsistency of input data, coarse resolution of demographic data, lack of in-depth study on scale effect, the scarcity of time series products and few validation works. To deal with these issues, more studies should be conducted to the following aspects: comprehension of population distribution mechanism, calculation of consistency and validation of existing datasets, application of multi-sources remote sensing data and volunteered geographic information, continuous space-time simulation of population distribution in the typical areas, sub-block-level population estimation, self-adaptive spatialization method which integrates multiple elements and multiple models. In summary, the research on spatialization of demographic data has made breakthroughs in the past two decades. Meanwhile, there are a few problems that need to be solved immediately. Since these two aspects had been reviewed as comprehensively as possible, we hope issues discussed in this paper could enlighten and promote the future study in this field.