Mixed Land Use and Obesity: An Empirical Comparison of Alternative Land Use Measures and Geographic Scales

Obesity is a growing epidemic in the United States. Walkable neighborhoods, characterized as having the three Ds of walkability (population Density, land use Diversity, and pedestrian-friendly Design), have been identified as a potentially promising factor to prevent obesity for residents. Past studies examining the relationship between obesity and walkability vary in geographic scales of neighborhood definitions and methods of measuring the three Ds. To better understand potential influences of these sometimes arbitrary choices, we test how four types of alternative measures of land use diversity measured at three geographic scales relate to body mass index for 4,960 Salt Lake County adults. Generalized estimation equation models demonstrate that optimal diversity measures differed by gender and geographic scale and that integrating walkability measures at different scales improved the overall performance of models.     

多尺度步行环境对居民肥胖的影响机理——以广州为例

基于2019年广州18个社区1 470份问卷数据,通过构建结构方程模型探究不同尺度步行环境（包括步行适宜性和其他与步行相关的建成环境要素）对居民肥胖的影响机理。研究发现：居民出行态度与偏好会影响通勤行为,但对居民肥胖没有显著的直接影响。在考虑了居民社会经济属性和出行态度偏好后,居住地步行环境对居民身体质量指数（body mass index, BMI）具有显著的负向总体效应。步行环境对居民BMI的影响具有尺度效应,社区尺度的步行适宜性影响最为显著。不同尺度的绿度、路网密度、地铁站密度和与城市中心的距离等其他与步行相关的建成环境均对居民BMI有不同程度的影响。     

三峡库区土地持续利用潜力与途径模式

建立了量化评价模型,对三峡库区土地资源持续利用的潜力及其差异性进行了评价与分析。指出库区土地利用总体上处于弱持续发展的阶段,而且地区差异悬殊、面临的障碍因素多。因此,未来库区土地利用优化决策的关键在于遵照因地制宜和分类指导的原则,正确处理生态建设、移民安置和经济协调发展的关系。主要的途径模式包括植树造林、坡地改梯田、优化结构和产业突破等四个方面。     

土地利用类型结构的多尺度转换特征

尺度一直是LUCC到GLP研究所关注的重要问题。以不同比例尺的辽宁省土地利用数据为例,以贵州省土地利用数据进行验证,系统探讨了土地利用类型结构在不同尺度间可能存在的转换特征。结论如下:(1)辽宁省土地利用的分形结构跨1 ∶ 10万、1 ∶ 50万、1 ∶ 100万这3个比例尺客观存在;(2)对不同比例尺的同一区域不同土地利用类型而言,各土地利用类型分维在不同比例尺之间存在的响应关系并不一致,某些土地利用类型的分维随着比例尺由1 ∶ 10万向1 ∶ 50万再向1 ∶ 100万的变小,分维呈现变大趋势,而某些土地利用类型的分维随比例尺的变化并不如此,所以,对于不同的土地利用类型而言存在一定的差别,不可一概而论;(3)不同尺度间的土地利用类型结构只能够在两两连续间隔的两种比例尺土地分维之间建立起关系显著的定量关系模型。所得结论对科学认知土地结构空间配置的多尺度特征以及建立相应尺度转换模型具有积极的参考意义。     

Analysis of the effects of land use change on protected areas in the Philippines

Deforestation and forest degradation are the most important land use change processes in the Philippines. These processes are an important threat to the highly rated biodiversity of the country. Only a small fraction of the natural forest that once covered the country remains. In spite of different policies that aim to reduce logging recent commercial deforestation, illegal logging and agricultural expansion pose an important threat to the remaining forest areas.In this paper we discuss the role of (land use) modeling approaches for assessing the threats and trade-offs of protecting the designated nature areas. At the national level different scenarios of land use change and implementation of the protected area policy are evaluated and discussed based on a spatially explicit land use allocation model. For one of the main national parks, the Northern Sierra Madre Nature Park, a detailed analysis is presented based on in-depth knowledge of the region. The two modeling approaches discussed in this paper aim at different scales and provide complementary types of information to support the planning and management of nature conservation strategies. The combination of land use change analysis at different scales respects the hierarchical organization of the land use system and addresses different levels of protected area management. The results indicate that land use change models are useful tools to inform protected area management as long as the selection of the model approach is based on the research and policy questions at the appropriate scale.     

Regarding research as a land use

Although missing as a land use category in all or most land classification systems, many land designation systems recognize research as the primary use of specific land areas. Research lands may be extensive and take up large areas; they may also be widespread systems of smaller designated sites that cover a significant total area. They are designated at the scale of international, national, subnational and smaller systems. New Mexico and Oregon, USA, illustrate extensive designation of lands for research purposes, including the variety of types and scales of research lands. In New Mexico, over a million hectares of land have been designated for research. This amounts to about 3.3 per cent of the state's area. Recognition of research as a land use, and of lands designated for research or scientific purposes, opens up many topics for further investigation.     

Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model

The extensive alteration of the earth's land cover during the anthropocene had widespread, and in some cases unknown, effects on terrestrial and atmospheric conditions and processes. Predicting future changes to the earth system therefore mandate a future-predicting framework of land use dynamics. However while future-predicting earth surface and atmospheric models tend to explicitly incorporate projected climatic conditions they all but ignore or overly simplify land use dynamics. As most surface and atmosphere dynamics models use gridded input datasets, and land use is a highly spatially-dynamic phenomena, a need clearly arise for spatially explicit representation of future land use dynamics. While a number of such datasets exists at regional and country scales, no fully gridded future-predicting global land use model and database has been reported to date. Here we present the Global Land Use Dynamics Model (GLUDM), a gridded and temporally explicit agricultural land use predictor. GLUDM calculates the relative area of a land use category (e.g. cropland) in each grid-cell by generating unique regression coefficients in each grid-cell based on local historic trends and global population dynamics. Spatial expansions or abandonment of agricultural land is simulated by propagating excesses or deficiencies in agricultural areas between neighboring grid-cells. This spatial connectivity is restricted by topographic, latitudinal and urban characteristics. A validation analysis shows that GLUDM corresponds well to observed land use distribution. GLUDM-predicted global cropland area dynamics between 2005 and 2100 are described herein. Globally, 18% increase in cropland area is predicted between 2005 and 2050 which corresponds very well to previous estimations. Following 2050, a general decrease in cropland area is predicted. The results reveal new insights about global cropland dynamics, demonstrating, for example, that changes in its spatial distribution will be highly heterogeneous, at both micro and macro scales, in some locations worldwide.     

区域土地利用转型诊断与调控的分析路径

土地利用转型正表现为城乡地域系统演变的重要特征,土地利用转型分析的意义在于发现土地利用形态转变指示的土地利用问题。本文尝试解析了区域人地耦合关系下的土地利用形态转型逻辑,系统阐释了区域土地利用转型的诊断分析路径,重点探讨了乡村土地利用转型问题诊断与调控途径。研究指出：① 区域土地利用转型侧重考察土地变化趋势性转折对应的土地利用方式的转变要求,阐释转型分析所指示的土地利用问题及其表现特征,以此找寻转型调控的应对策略。② 应依据尺度和梯度分异方式揭示区域土地利用转型特征,理解不同尺度上转型调控的融合过程,研判区域土地利用转型调控措施。③ 土地服务供给变化是引发乡村土地利用显性形态和隐性形态转型发生错位的关键原因,土地利用显性形态与隐性形态之间的互适关系,则反映了土地利用形态空间关系构成的问题特征。④ 基于乡村地域尺度和梯度分异原则、基于城乡融合发展背景、重构农村土地权利体系和开展国土空间规划是探索乡村土地利用转型调控的四个重要分析路径。⑤ 不同尺度区域土地利用转型认知与转型调控对策的融合仍需要进一步拓展,而激活乡村土地资源要素是调控乡村土地利用转型的关键途径。     

环渤海地区土地利用效益综合测度及空间分异

土地利用的多功能性决定了土地利用效益的多样化。通过构建土地利用效益评价指标体系、效益测度指数与耦合协调发展度模型,探讨了环渤海地区县域土地利用的社会效益、经济效益、生态效益、耦合格局及其协调发展特征。研究表明:环渤海地区土地利用效益的空间分异性与集聚性特征显著;经济社会中高值区主要分布在沿海地区和内陆平原区,生态高值区以冀西北间山盆地、坝上高原和辽宁山地丘陵区为主;从土地利用效益耦合度类型看,环渤海地区磨合阶段区域数量最多,占研究区的63.9%,集中分布于冀中冀南和山东省;低度耦合、拮抗阶段的区域呈带状环绕环渤海北部地区;高度耦合区域呈现出散乱的点状空间分布格局;协调发展度指数显示,轻度失调区占据主导地位,其次是初级协调区域,滞后失调区域数量较少,分别占环渤海地区总面积的51.09%、31.76%和10.87%。通过划分土地利用类型区,制定差别化区域土地利用政策,加强区域土地管理与规划,建立健全区域利益协调与补偿机制,是今后统筹协调土地利用的社会经济生态效益的重要方向。     

土地利用现状数据缩编的质量评价方法

土地利用现状数据缩编是为了满足土地管理工作中不同级别的行政区需求对不同尺度的土地利用数据进行的简化、分类、归纳、符号化等工作,缩编应能使不同尺度的数据表达的土地利用信息保持最大程度的一致,缩编的质量直接影响缩编结果可用性.本文提出了缩编结构相似度指数、缩编布局相似度指数、缩编罔斑适宜度指数辅助传统的相关性统计指标,用来...     

土地持续利用研究进展

自1990年印度农业研究会、美国农业部和美国Rodale研究所在新德里举行的首次国际土地持续利用研讨会上提出土地持续利用思想以后,国内外众多学者和研究机构对土地持续利用展开了广泛的研究。在明确土地持续利用概念和内涵的基础上,本文回顾、总结了国内外土地持续利用研究进展：首先阐述了土地持续利用研究的目标和内容,其次从土地合理利用的系统分析、土地合理利用评价和综合研究模型三个方面论述了国际土地持续利用研究进展,并且对国内近年来土地持续利用的研究内容进行分类总结和评述,最后阐述了目前研究中的问题和今后研究展望。     

基于步行性与污染物暴露空间格局比较的建成环境健康效应——以南京为例

在雾霾问题日益严重的中国,通过提高建成环境的步行性以增加居民户外体力活动的同时,也会增加居民的污染物暴露度,最终的健康效应不一定为正。针对这一悖论,论文以南京为例,通过步行指数测度步行性,基于土地利用回归模型和插值模拟PM_2.5和O₃浓度的空间分布,在此基础上比较步行性与污染物暴露的空间关系,从而对建成环境健康效应进行评价。研究表明：确实存在高步行性但同时也是高污染物暴露的城市空间,这部分空间主要分布在城市中心区,在这些地区片面强调提高步行活动其实是存在很大健康风险的;城市郊区则呈现低步行性且低污染物暴露的特征,其健康效应亦难以直接判断;靠近城市中心但又距其有一定距离的大面积绿地周边地区呈现高步行性且低污染物暴露的特征,其健康效应为正;而城乡交界的半城市化地区容易成为低步行性且高污染物暴露的健康效应为负的空间。研究结果可以为精准制定健康城市的土地利用政策提供参考依据。     

Population-adjusted street connectivity,urbanicity and risk of obesity in the U.S.

Street connectivity, defined as the number of (3-way or more) intersections per area unit, is an important index of built environments as a proxy for walkability in a neighborhood. This paper examines its geographic variations across the rural-urban continuum (urbanicity), major racial-ethnic groups and various poverty levels. The population-adjusted street connectivity index is proposed as a better measure than the regular index for a large area such as county due to likely concentration of population in limited space within the large area. Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS), this paper uses multilevel modeling to analyze its association with physical activity and obesity while controlling for various individual- and county-level variables. Analysis of data subsets indicates that the influences of individual and county-level variables on obesity risk vary across areas of different urbanization levels. The positive influence of street connectivity on obesity control is limited to the more but not the mostly urbanized areas. This demonstrates the value of obesogenic environment research in different geographic settings, helps us reconcile and synthesize some seemingly contradictory results reported in different studies, and also promotes that effective policies need to be highly sensitive to the diversity of demographic groups and geographically adaptable.     

太仆寺旗土地利用变化时空格局的动态模拟

本文以太仆寺旗为研究区 ,通过将土地利用驱动因子分解为稳定少动控制因子、年际变动影响因子与社会经济驱动因子 ,求解了太仆寺旗土地利用变化驱动因子作用系数矩阵 ,揭示了不同类型因子驱动土地利用变化的方向与强度。在此基础上 ,以CLUE S模型为框架 ,构建了太仆寺旗土地利用变化时空格局模拟模型 ,通过集成基于太仆寺旗土地利用系统动力学模型获取的土地利用变化及其社会经济驱动因子信息 ,动态模拟了太仆寺旗土地利用变化的时空模式 ,进行了参考模式、生态模式与经济模式下的情景分析。     

Characterizing streamflow response of a mountaintop-mined watershed to changing land use

Mountaintop removal mining is a dominant driver of land use/land cover changes in the Appalachian Region of the eastern United States and is expected to increase in scale in the coming decades. While several studies quantify land use/land cover changes attributed to traditional surface mining and at regional scales, no studies we are aware of focus specifically on mountaintop removal/valley fill mining practices at the watershed scale. Further, despite its scale and extent, its impact on runoff, particularly at larger spatial scales (10³ km²), is poorly understood due to the complex relationships between climate, land use, and hydrology. To explore the impacts of this practice at broader scales, we estimated land use/land cover changes using Landsat 5 TM imagery over five periods between 1994 and 2010; used a simple rainfall–runoff model to estimate hydrologic response time; and conducted non-parametric trend analyses on annual hydrologic metrics (streamflow, Q/P, response time) for the Big Coal River watershed located in the southern West Virginia coalfields. No statistically significant trends were detected in any of the timeseries. The lack of detectable trends and correlations between land use changes and hydrology at the basin scale are not entirely unexpected due to the history and mosaic of land cover changes that span timescales larger than our study period. Further interannual variation likely overwhelms our ability to detect potential changes using monotonic trend analysis at the annual time scale, particularly in light of strong streamflow seasonality. Future studies therefore should include different methods of change detection applied to different timescales to more appropriately account seasonal and interannual variations. Until the significance of this practice on water resources (quality and quality) are understood, efforts to reduce the environmental problems associated with mountaintop mining will be difficult to achieve.     

土地整治与乡村振兴——土地利用多功能性视角

长期以来乡村土地利用功能的供需不平衡是造成乡村问题的主要原因之一。论文基于土地利用多功能性视角,以“供给—需求”“要素—结构—功能”为主线,阐释了农村土地整治与乡村振兴的互馈关系,探讨了如何通过农村土地整治实现乡村土地利用功能的供需平衡,进而促进乡村振兴。研究结果如下：① 综合土地整治是一种多功能的土地利用方式,是当前解决乡村问题、促进乡村振兴的重要手段,其本质是从以经济效益为主的生产主义向兼顾社会、经济、环境等的非生产主义的转变。② 乡村土地利用具有生产、生活、生态、文化等4种主要功能,满足乡村振兴在经济、社会、环境、文化等方面的需求,其中生产功能分为农业、商业、工业生产功能,生活功能分为居住、就业和公共服务功能。③ 农村土地整治沿整合土地利用要素、重组土地利用结构、优化土地利用功能的路径,从供给侧因地制宜地平衡乡村发展对土地利用多功能的需求。④ 今后,农村土地整治与乡村振兴的研究应该考虑不同空间尺度下土地整治对于乡村振兴的作用机制与模式,定量分析土地整治影响下土地利用的多功能供给以及不同类型乡村发展对土地利用功能的需求,从而为土地利用和乡村振兴规划的制定和实施提供科学依据。     

论土地利用转型与土地资源管理

自21世纪初土地利用转型这一研究方向引入中国后,土地利用转型已成为当今学术界和国家行政部门十分关注的重要课题。在拓展深化土地利用转型的概念内涵,阐述土地利用转型的理论模式基础上,探讨了土地利用转型与土地资源管理二者之间的互馈机制,进而分析了土地利用转型与土地资源管理二者之间的相互影响。研究指出：决策部门应根据土地利用形态的变化适时调整土地资源管理政策措施,充分考虑目标区域所处的土地利用转型阶段,以增强土地资源管理决策的科学性。在探讨未来土地利用转型与土地资源管理研究方向基础上,强调土地利用隐性形态及其变化应当成为今后土地利用转型与土地资源管理研究关注的焦点,通过管控土地利用隐性形态的变化来创新土地资源管理政策法规及制度,提升土地资源管理水平。     

The development and application of a land use diversity index for Oklahoma City,OK

Characteristics of land use and land use diversity play an integral part in determining how a city develops, and also can influence the allocation of funds for urban redevelopment, crime prevention, and civic and transportation projects. With the rise of the sustainability movement and concern about environmental degradation, more cities are interested in optimizing transportation routes and utilizing their budgets to accommodate high-priority projects that benefit the most people. This paper develops a method for measuring land use diversity for Oklahoma City, Oklahoma, and explores the relationship between land use diversity and a suite of demographic variables. To accomplish this, a land use diversity index (LUDI) was developed to examine spatial patterns in diversity of land use. The analysis shows that areas with a higher LUDI are near interstates and main corridors while areas with a lower LUDI are located closer to where newer residential areas have been developed. A multivariate statistical approach was then used to identify those variables that might explain the specific spatial patterns of the LUDI in Oklahoma City. The results identify the relationship between the demographic variables and land use across the city, and illustrate the most interesting and significant spatial patterns. By coupling the land use diversity index and demographic data, Oklahoma City planners can apply the information to help make important decisions for the city, and it will provide quantifiable data to support their decisions.     

Typologies of sprawl: investigating United States metropolitan land use patterns

We investigate patterns of residential and nonresidential land use in 311 United States metropolitan (Extended Urban) areas in 2000 using four measures: intensity, compactness, mixing, and core-dominance. A cluster analysis revealed four distinctive groups of land use patterns: (1) Most-Intense, Least-Compact, Least-Mixed, More-Monocentric Development, (2) Less-Intense, Most-Compact, Less-Mixed, Less-Monocentric Development, (3) Least-Intense, Less-Compact, Most-Mixed, Most-Monocentric Development, (4) More-Intense, More-Compact, More-Mixed, Polycentric Development. Bivariate statistics demonstrated that geographic, historic, economic, demographic, and transport variables differentiate land use pattern types. Based on their multidimensional distinctions, we label the four types of metropolitan areas: Ascendants, Insulars, Redevelopers, and Cosmopolitans.     

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.