DATA150-azhao02

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Urbanization is an inevitable process in human development. It represents a comprehensive increase in not only the scale of urban areas, but indicating more on a complicated perspective. Considering the influence brought by the application of the Hukou system, there is a great population shifting from rural areas to urban areas. Based on that, the scale of the social structure becomes more complicated and many problems of the effectiveness of the food supply chain between regions. How to avoid waste of food resources and stimulate the food production industry. The type of inquiry that investigates a geospatial human development process can be considered as an exploratory inquiry. When talking about the urbanization process of China in recent few decades, a topic that cannot be avoided is the inner population shifting brought by it. The key part of this process is to analyze the impact brought by the urbanization process, and the entry point of that is to take a deeper look at the internal connection between urbanization and food supply related issues. Because of the distance and interval between urban and rural areas, especially when considering the origin of various food production allocations, how to effectively distribution the food resources can be a critical problem. Other issues that are associated with that is to avoid waste under this huge social environment and how the needs from urban areas will impact the food production around.

Considering the geospatial data features, the focusing area of this passage is in the north east part of China and around Beijing, which is the capital as well as the economic and political center of China. In the research article Changing man-land interrelations in China’s farming area under urbanization and its implications for food security, it mainly works on The Huang-Huai-Hai Plain, also known as HHH. It is a typical Chinese farming area, and was predicted as one of the fastest growing areas under urbanization all over the world. HHH is close to the capital Beijing, and works as a significant food production area of China. So the geospatial data analysis can be a typical case and worth of study. For its research method, after located the stud area, the author obtained the land use data which classification in 2000, 2005, 2010 and 2015. The dataset contains 6580 town-level resident population data and population density sampling data in the HHH. This research processes the data through PCCT and PCFT. The land-use data of it is obtained from Landsat TM and ETM true color composite image after geometric rectification and supervised classification. When running the logrism process, the researchers bring out two terminologies which are PCCA that stands for per capita construction land area in m^2 /person and PCFA that stands for per capita farmland area in ha/person. The equations of them respectively are Conarea and Farmarea divided Podensity, where Conarea and Farmarea represents the construction and farm land area in each 1 kilometer times 1 kilometer grid. And the Podensity is the population size in person for each grid. After processing the mathematical equation that PCCT=PCCAt2-PCCAt1 and PCFT=PCFAt2-PCFAt1, which t1 and t2 represents for the starting and ending period, many informations are shown through a deeper analysis of the grid scale correlation of PCCA and PCFA. The correlation coefficient could be estimated by:

It should be acknowledged as a kind of spatial econometric regression analysis which analyzes the influential factors of the interactional relationships between PCCT and PCFT at grid-scale through the spatial econometric regression models. Although this research uses spatial economic regression analysis, it can be considered as both spatial and temporal dimension. This research does not include a survey but does use secondary data. The origin of the data is reliable and valid since it was taken from the China Natural Resources Data Center, the National Basic Geographic Information Center, and so on.

Except for that, the migration pattern of Chinese internal population is shifting worthy of analysis. In Spatial shifts in grain production increases in China and implications for food security, it focuses on two perspectives. First of all, the social status and labour market, mostly rural migrants in destinations, will greatly impact the migration pattern. Especially when considering a general nature of capitalist accumulation in China. After that, the underlying cause is another factor that is worth a great concern. During this research, it employs exploratory spatial data analysis, which also can be known as ESDA. This method is an extension of exploratory data analysis as it focuses on the particular characteristics of geospatial data. It generally based on a formula:

This method basically works on the changes of geospatial patterns, such as population, economic, and energy barycenter, and also frequently used for spatial analysis. The data is taken from the China County Statistical Yearbook of 2004, 2006, 2011 and 2015 Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC), which ensures the validity and reality of the whole research.

For this method, it includes global spatial autocorrelation, known as Global Moran’s I, and local spatial autocorrelation, known as Local Moran’s I. It uses Software GeoDA to calculate the Index of grain output growth in all phases of the Global Moran I, and the calculating equation of that is working in reference to the correlation between indexes.

In the final step of the research, it uses the Local Indicators of Spatial Association, known as LISA to examine the geospatial associations. In general, this research is more likely to refer to spatial research with secondary data usage. The official origins of its data resources also ensure its validity and reliability.

When comparing these two methods, both of them have advantages. However, although both of them are working on the same issue of the urbanization process of China and its related problems such as population shifting and so on. And both researchers use geospatial data science methods. But compared with the first one, the second method is kind of broad. The analysis of the second paper is very significant in the background of great China, but the first one is focusing on the HHH area which is also close to the capital Beijing. It is more focused on the area of the topic of this project. And the reason for choosing Beijing and its surrounding area is that it can be considered as the economic and political center of China. Which also means that the urbanization process can be present very well in this area. Also, based on the research, during the great internal migration time period, there are generally three great migration directions, which are Beijing, Guangzhou, and Shanghai. Because of the political significance of Beijing, there is reason to believe that the result can be representative.

However, in a nutshell, both researches present a result of that there do have some issues associated with urbanization that need to be worked on for Chinese food production. Based on the data analysis, as the barycenter moves northwards, typically the region of north bank of the Yellow River, grain production gradually takes the dominant status of agriculture for the whole nation. However, considering the dominant factors of grain production such as weather factors and water supply, this kind of spatial shifting trends actually lower efficiency of natural reproduction with higher risk of natural disaster for grain due to poor environmental conditions in those areas. On the contrary, some areas with proper natural conditions are not effectively used. This might be one of the greatest vulnerabilities of China’s food security. Also, another problem is the possible waste happening in urban areas.

Based on the study, it is obvious that the needs of food are great in urban areas. Considering the distance gap between urban and rural areas, and the needs of transportation in the food supply chain, a more effective and direct channel to connect the farmer in rural areas and customers in urban areas became significant for future development in Chinese food supply issues. It does cause some problems under this kind of situation. Because of the unequal distribution of resources between urban and rural areas, as well as the difficulties of shipping and transportation, the selling of productions cannot effectively go through a B2B way. Also, it usually presents an unequal between supply and demand. For nowadays agricultural situations, a very typical situation is that when the farmers find out a special kind of production can bring a huge benefit, they are very likely to greatly improve the production quantity of that. Increasing in supply definitely will cause a decreasing in price, as well as the profit. In today’s society, most urban areas are showing over consumption which causes food waste. They are willing to pay a high price for a product, but actually do not need a huge quantity for that. The gap of the information between rural and urban sometimes will cause a great number of unsaleable products for farmers. And this situation becomes more and more obvious along with the urbanization process. So it will be a good way to use the GMM model, which is a good way that allows flexible definitions and able to estimate the future trends from existing moments of the whole data set. It is a proper method for this case since the past few decades of Chinese imgration trend is worth analysis.

##Work Cited##

Wang, J., Zhang, Z., & Liu, Y. (2018). Spatial shifts in grain production increases in China and implications for food security. Land Use Policy, 74, 204-213. doi:10.1016/j.landusepol.2017.11.037

Long, H., Ge, D., Zhang, Y., Tu, S., Qu, Y., & Ma, L. (2018). Changing man-land Interrelations in CHINA’S farming area under urbanization and its implications for food security. Journal of Environmental Management, 209, 440-451. doi:10.1016/j.jenvman.2017.12.047