第二讲城市规划定量分析方法：空间分析和建模主讲人沈青博士美国马里兰大学教授城市研究与规划
第一部分. 空间分析PART I. SPATIAL ANALYSIS•Using a set of mathematical, statistical, and GIS (geographic information systems) tools for studying spatial distributions, relationships, and interactions•Usefulness for urban planning–Understanding the city–Supporting location choice for social services–Evaluating effects of land use plans, and infrastructure and transportation investments
常用方法FREQUENTLY APPLIED METHODS•Accessibility measures•Service area delineations•Spatial association / segregation measures
例子EXAMPLE OF APPLICATION•A study of low-income people’s geo-spatial position in the urban labor market (Shen 1998, 2001)•Background–Alternative policy strategies for helping low-income access employment opportunities–Debate on “spatial mismatch”–Welfare reform
问题QUESTIONS•Where are employment opportunities located in . metropolitan areas?•How does employment accessibility vary?–Among residential locations–Among travel modes
方法METHODOLOGY•Analytical tools–Accessibility measures–3-D GIS visualization•Case study and data–Boston metropolitan area–Data on labor force, employment, transportation, etc.
BOSTON METROPOLITAN AREA波士顿都市区
分析结果RESULTSThe Composition of Job Openings Positions Vacancies from Total Job from Growth Turnover Openings Total 1,64031,28032,910Low-Skilled 290 10,400 10,690 • Employment growth accounts for only 5% of total jobopenings, and only 3% of job openings for the low skilled.• Turnover is the dominant source of job openings.
分析结果(续) RESULTS (Continued)Spatial Distributions of Job Openings and Seekers Whole MetroWithin BostonOutside BostonPositions Created29020270by Growth(%)(%)Vacancies Created10,4002,1208,280by Turnover(%)(%)Job Openings for10,6902,1408,550Low-Skilled(%)(%)Low-Skilled50,48010,65039,830Seekers(%)(%)
就业量变化导致的低收入工作机会分布图
劳动力流动导致的低收入工作机会分布图
低收入工作机会总量分布图
低收入失业者总量分布图
低收入工作机会与失业者比率图
Map 6. Automobile Commuters' Accessbility to Job Openings私人汽车拥有者的就业可达度 - - - - - and over
Map 7. Transit Commuters' Accessbility to Job Openings公共交通依赖者的就业可达度 - - - - - and over
分析结果(续) RESULTS (Continued)Spatial Variation in Job Accessibility • For a given location, accessibility for auto drivers is usually much higher than for transit riders • For seekers who can commute by car, most locations give them good access to job openings • For seekers who depend on public transportation, only very few locations give them good access to job openings • For a given travel mode, living in the central city—including its low-income neighborhoods—still has some advantage in access to job openings
第二部分城市建摸PART II. URBAN MODELING•Developing mathematical equations and analytical procedures for predicting future patterns of urban growth under different assumptions•Usefulness for urban planning–Understanding quantity and location of future land consumption–Assessing transportation and environmental impacts of urban growth–Simulating effects of land use regulations, and infrastructure and transportation investments–Planning for efficient and equitable urban growth
通用方法COMMON APPROACHES•Spatial interaction models, with transportation analysis zones-based growth allocation mechanisms•Urban growth models, with rich GIS data and rules or probability functions for growth allocation.•Spatial economic models, with utility maximization procedures for growth allocation
例子EXAMPLE OF APPLICATION•California Urban Futures Models (Landis 1994, 1995, Landis and Zhang 1998, Landis 2000)•Background–Rapid population growth and urbanization in California–Concerns over environmental and ecological impacts of growth–Infrastructure and service investments–Inter-jurisdictional issues and fiscal impacts–Alternative policy initiatives for regulating future growth
目标OBJECTIVES•Develop a spatially-explicit model for projecting urban growth•Frame alternative development scenarios•Use model to explore alternative scenarios
模型输入MODEL INPUTS•Map Layers (unit-of-analysis: 1 hectare grid-cell)–Initial employment and population distributions–Current land use–Environmental characteristics (prime agricultural lands, wetlands, public lands, flood zones, slope)–Policy characteristics (administrative boundaries, zonings, development fees, etc.)–Infrastructure (freeways, roads, transit services, etc.)•Activity Projections–Regional population and employment projections•Scenarios–Baseline, Compact Growth, Sprawl
模型参数测定MODEL CALIBRATIONProb[grid cell changing land use between time t-1andt]= ƒ{ Proximity to highways (+)Proximity to initial urban development (+)Proximity to city center (+)Site slope (-)In flood zone (-)In wetlands (-)Prime or unique farmland (-)Percent of urbanized neighboring cells (+)… }Tested using bi-nomial / multi-nomiallogistic regression model {changed = 1, no change in undeveloped status = 0}
2020 Southern California Urban Footprint Scenarios: Greenfield Shares and Allocation Densities1.新开发区人口增长所占比例,2.人口密度Projected Baseline Baseline Compact CompactSprawl Sprawl CountyPopulation Greenfield Allocation Growth Greenfield Allocation Growth Growth, 1997-ShareDensityGreenfield ShareDensityAllocation 2020ShareDensityLos 2,051,00033%30 p/ha33%41 p/ha67%30 p/haAngelesOrange727,00050%2550%3175%25Riverside1,350,00095%1579%1998%15San 1,130,00095%779%998%7BernardinoSan Diego1,153,00090%2275%2795%22Ventura254,00060%1960%2480%19
三种方案下的人口增长分布
Southern California 2020 Footprint: Baseline Scenario: Projected Prime Farmland Conversion 优质农业用地损失VenturaProjected 2020 ConversionSan Diego1996 HectaresSan BernardinoRiversideOrangeLA County020,00040,00060,00080,000Hectares
Southern California 2020 Footprint: Baseline Scenario: Projected Loss of Prime Multi-ESA Habitat环境敏感区损失VenturaSan DiegoProjectedSan Bernardino2020ConversionRiverside1996HectaresOrange050,000100,000150,000200,000250,000300,000Hectares
发展方向FUTURE DEVELOPMENTS•Technology–Technology (computer, GIS, remote sensing, etc.) as tool for spatial analysis and modeling–Technology as a key factor influencing location and travel behaviors (telecommuting, firm relocation, etc.)•Social issues (divergence among socio-economic groups in location preferences and options)•Inter-jurisdictional relations and fiscal impacts•Environment and sustainable development•More complete and integrated analytical tools and models
