申庆喜, 李诚固, 马佐澎, 周国磊, 胡述聚.
Scientia Geographica Sinica,
[Shen Qingxi, Li Chenggu, Ma Zuopeng, Zhou Guolei, Hu Shuju.
The Expansion of the Functional Space of Changchun City Based on Service Space[J].
Scientia Geographica Sinica,
The Expansion of the Functional Space of Changchun City Based on Service Space
Shen Qingxi,, Li Chenggu,, Ma Zuopeng, Zhou Guolei, Hu Shuju
National Nature Sciences Foundation of China(41171103) and the Fundamental Research Funds for the Central Universities（2412015KJ022）;
In the background of rapid urbanization, the expansion of urban functional space is causing widespread concern. The rapid expansion of urban area brings about a series of social and ecological problems. The focus of study is mismatch between expansion of urban area and service facilities. Our study makes use of land status maps and statistical information regarding Changchun City. It is also discussed that the features, effects and driving mechanisms of the functional space expansion of Changchun by citing a variety of models and spatial perspectives based on the availability of service space since 1995. ArcGIS and SPSS were used to reach and support our findings. The study’s findings are as follows: 1) The urban space of Changchun City expanded rapidly between 1995 and 2013. The pattern of urban expansion is still displaying the close field extension known as “circle mode”. Overall deviation problems between the city and service space have always existed. In the process of urban development and construction, the imbalance between functional space and urban space becomes more prominent. 2) The rapid expansion of Changchun’s urban space sparked a series of negative effects, including urban sprawl and an imbalance in urban functional space. However, expansion also promotes the evolution of functional space. The polycentric intra-urban structure has emerged in the metropolitan area. 3) This study also probes the dynamic mechanism of the evolution of the functional space of Changchun City, including administrative and socio-economic factors. We conclude macro urban development strategy results in mismatch between urban and service space, that policy reform promotes expansion of urban area, that socio-economic development is the source for the evolution of urban space. Through the establishment of regression model, the study shows that urbanization in terms of people and the improvement of urban living environments are social and economic core factors of the expansion of the essential connotation of urban functional space.
[YanMei, HuangJinchuan.Review on the research of urban spatial expansion. , 2013, 32(7): 1039-1050.]
Hoffhine WilsonEmily, Hurd JamesD, CivcoDaniel Let al. Development of a geospatial model to quantify, describe and map urban growth[J]. , 2003, 86(3): 275-285.
The output urban growth map is a powerful visual and quantitative assessment of the kinds of urban growth that have occurred across a landscape. Urban growth further can be characterized using a temporal sequence of urban growth maps to illustrate urban growth dynamics. Beyond analysis, the ability of remote sensing-based information to show changes to a community's landscape, at different geographic scales and over time, is a new and unique resource for local land use decision makers as they plan the future of their communities.
Seto KC, Kaufmann RK.Modeling the drivers of urban land use change in the Pearl river delta,China:Integrating remote sensing with socioeconomic data[J]. , 2003, 79(1): 106-121.
Clarke KC, HoppenS, GaydosL.A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area[J]. , 1997, 24(2): 247-261.
In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.
CrooksA, CastleC, Batty MKey.Challenges in agent-based modelling for geo-spatial simulation[J]. , 2008, 32(6): 417-430.
MillwardHugh.Urban containment strategies: A case-study appraisal of plans and policies in Japanese, British, and Canadian cities[J]. , 2006, 23(4): 473-485.
The case studies demonstrate more stringent control on the location, timing, and density of development in Britain and Japan, with shorter time horizons and tighter development boundaries than in Canada. The Canadian cities, however, are moving towards higher densities, to enable transit-oriented development.
[ZhaoLu, ZhaoZuoquan.Projecting the spatial variation of economic based on the specific ellipses in China. , 2014, 34(8): 979-986.]
LuckMatthew, WuJianguo.A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA[J]. , 2002, 17(4): 327-339.
<a name="Abs1"></a>Urbanization is arguably the most dramatic form of land transformation that profoundly influences biological diversity and human life. Quantifying landscape pattern and its change is essential for the monitoring and assessment of ecological consequences of urbanization. Combining gradient analysis with landscape metrics, we attempted to quantify the spatial pattern of urbanization in the Phoenix metropolitan area, Arizona, USA. Several landscape metrics were computed along a 165 km long and 15 km wide transect with a moving window. The research was designed to address four research questions: How do different land use types change with distance away from the urban center? Do different land use types have their own unique spatial signatures? Can urbanization gradients be detected using landscape pattern analysis? How do the urban gradients differ among landscape metrics? The answers to these questions were generally affirmative and informative. The results showed that the spatial pattern of urbanization could be reliably quantified using landscape metrics with a gradient analysis approach, and the location of the urbanization center could be identified precisely and consistently with multiple indices. Different land use types exhibited distinctive, but not necessarily unique, spatial signatures that were dependent on specific landscape metrics. The changes in landscape pattern along the transect have important ecological implications, and quantifying the urbanization gradient, as illustrated in this paper, is an important first step to linking pattern with processes in urban ecological studies.
[LiYiman, XiuChunliang, WeiYeet al. Analysis on mechanism and spatial-temporal features of urban sprawl: a case study of changchun. , 2012, 32(5): 59-64.]
Seto KarenC, FragkiasMichail, GüneralpBuraket al. A meta-analysis of global urban land expansion. , 2011, 6(8): e23777.
The conversion of Earth's land surface to urban uses is one of the most irreversible human impacts on the global biosphere. It drives the loss of farmland, affects local climate, fragments habitats, and threatens biodiversity. Here we present a meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion. We report a worldwide observed increase in urban land area of 58,000 km2 from 1970 to 2000. India, China, and Africa have experienced the highest rates of urban land expansion, and the largest change in total urban extent has occurred in North America. Across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates, suggesting that urban growth is becoming more expansive than compact. Annual growth in GDP per capita drives approximately half of the observed urban land expansion in China but only moderately affects urban expansion in India and Africa, where urban land expansion is driven more by urban population growth. In high income countries, rates of urban land expansion are slower and increasingly related to GDP growth. However, in North America, population growth contributes more to urban expansion than it does in Europe. Much of the observed variation in urban expansion was not captured by either population, GDP, or other variables in the model. This suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs. Using the results from the global model, we develop forecasts for new urban land cover using SRES Scenarios. Our results show that by 2030, global urban land cover will increase between 430,000 km2 and 12,568,000 km2, with an estimate of 1,527,000 km2 more likely.