Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model

Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithm by considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes.     

Classifying urban land use by integrating remote sensing and social media data

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.     

城市土地潜力分析--以南京市为例

研究城市土地潜力是城市建设者合理利用城市土地、提高城市土地利用效率、保护耕地的基础。该文利用GIS对土地、规划、环保、人口等相关数据进行整合,从房地产开发的角度建立测算最低经济容积率的模型,求得每块城市土地的经济容积率;基于GIS空间分析功能,分析城市土地利用现状与最低经济容积率,并用地域环境质量综合评价、历史文化名城保护规划等数据对结果进行修正,得出南京市城市土地潜力的数量、质量及空间分布。     

When Fields Revert to Forest: Development and Spontaneous Reforestation in Post-War Puerto Rico

During the past 50 years forests have recolonized extensive areas of Puerto Rico. Between 1950 and 1990 forest cover increased from 9% to 37% of the island's land area. In proportional terms more land has reverted to forest in Puerto Rico than anywhere else on earth during the second half of the twentieth century. This paper explores the geography of this process by matching changes in land cover with the characteristics of the land and communities in Puerto Rico. The reversion of agricultural lands to forest occurred most frequently in humid, upland, coffee-growing regions characterized by heavy out-migration and populations of smallholders who earned some of their income from off-farm sources. These findings suggest that changes in non-farm labor markets, as well as changes in the political economy of agriculture, have important impacts on the prospects for converting agricultural lands into forests.     

When Fields Revert to Forest: Development and Spontaneous Reforestation in Post‐War Puerto Rico

During the past 50 years forests have recolonized extensive areas of Puerto Rico. Between 1950 and 1990 forest cover increased from 9% to 37% of the island's land area. In proportional terms more land has reverted to forest in Puerto Rico than anywhere else on earth during the second half of the twentieth century. This paper explores the geography of this process by matching changes in land cover with the characteristics of the land and communities in Puerto Rico. The reversion of agricultural lands to forest occurred most frequently in humid, upland, coffee‐growing regions characterized by heavy out‐migration and populations of smallholders who earned some of their income from off‐farm sources. These findings suggest that changes in non‐farm labor markets, as well as changes in the political economy of agriculture, have important impacts on the prospects for converting agricultural lands into forests.     

Spatiotemporal analysis of rural–urban land conversion

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.     

Optimal land use allocation of urban fringe in Guangzhou

In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms.     

广州市城市边缘区土地利用优化配置(英文)

In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms.     

城镇土地利用潜力评价——以广州市天河区为例

在我国城镇土地利用从外延扩展走向内涵挖潜、从粗放扩张转向集约利用的时代背景下,开展城镇土地利用潜力评价是实现城镇土地集约利用的前提.通过对现有研究的剖析,提出城镇土地利用潜力的概念,明晰城镇土地利用潜力的内涵.在此基础上,综合分析影响城镇土地利用潜力的因素,构建城镇土地利用潜力评价的指标体系,并基于GIS建立一套城镇土地利用潜力评价的技术方法、流程和评价模型.以广州市天河区为例,对其土地利用潜力进行了系统评价,评价结果显示,天河区345个评价单元中,适度利用、过度利用、低度利用和不均衡利用等4种潜力类型的土地单元个数分别占总评价单元的44.9%,15.9%,35.7%和3.5%,可供挖掘的潜力很大,并基于评价结果提出了相应的政策措施.通过对广州市天河区城镇土地利用潜力的评价,不仅为天河区的城市建设和土地利用提供依据,也为城镇土地利用潜力评价的推广和应用提供方法和思路.     

Agent-based model of land system: Theory,application and modelling framework

Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems. As a process-oriented modelling approach, agent based model(ABM) plays an important role in revealing the driving forces of land change and understanding the process of land change. This paper starts from three aspects: The theory, application and modeling framework of ABM. First, we summarize the theoretical basis of ABM and introduce some related concepts. Then we expound the application and development of ABM in both urban land systems and agricultural land systems, and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region, China. On the basis of combing the ABM modeling protocol, we propose the land system ABM modeling framework and process from the perspective of agents. In terms of urban land use, ABM research initially focused on the study of urban expansion based on landscape, then expanded to issues like urban residential separation, planning and zoning, ecological functions, etc. In terms of agricultural land use, ABM application presents more diverse and individualized features. Research topics include farmers' behavior, farmers' decision-making, planting systems, agricultural policy, etc. Compared to traditional models, ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data. However, due to its unique bottom-up model structure, ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.     

城市土地利用演化CA模型的扩展研究

城市土地利用演化的实质是人为干预下城市生态景观的自组织机制作用过程。根据城市土地利用演化生命机制概念,构建基于地理特征的城市土地利用演化CA模型,并以深圳特区为试验区域进行实证研究。结果表明,基于城市生命机制和地理特征的城市土地利用演化CA模型,可以有效地进行城市土地利用演化的时空模拟与预测。     

土地系统多主体模型的理论与应用

土地变化科学是理解人类—自然耦合系统的交叉学科研究方向。多主体模型（ABM）作为过程导向模型,对揭示土地变化驱动力,理解土地变化过程有重要作用。本文从理论、应用与建模框架三方面出发,总结了ABM理论基础和相关概念;阐述了ABM在城市和农业土地系统两方面的应用与发展,进一步介绍了横断山区退耕还林ABM研究案例;在梳理ABM建模协议的基础上,提出了主体视角的土地系统ABM建模框架和实施流程。在城市土地利用方面,ABM研究从最初基于景观研究城市扩张,到研究城市内部居住分隔,规划分区,生态功能等多方面;在农业土地利用方面,ABM应用则呈现出更加多样化和个性化的特征,包括农民行为、农户决策、种植系统、农业政策等。相比于传统模型,ABM因其依靠本地知识与数据而使得其构建更为复杂,且不易推广;但因其独特的自下而上模型构架,在探究土地变化驱动力、刻画人类行为对自然环境影响等方面具有不可或缺的作用。     

城市交通与土地利用一体化模型的理论基础与发展趋势

城市交通与土地利用间的协调发展是科学调控城市发展的重要环节。在基于地租理论的微观区位决策与土地利用与交通系统宏观动态交互理论基础上,回顾了城市交通与土地利用一体化模型的发展过程及不同依据下的分类特征,对空间相互作用和重力模型理论模型、空间经济学理论模型、随机效用和离散选择模型和基于行为的微观模型4类一体化模型的优劣进行了系统梳理分析。分析表明,具有合理理论基础、科学数据处理方法、较高模型精度和政策评估适用性的一体化模型仍存在巨大发展空间。近年来西方学者研究趋向微观化、综合化和简约化,而国内一体化模型发展相对滞后,要求模型理论与综合实证开发的进一步突破。特别是在新数据、新交通技术、国土空间规划体系背景下,城市交通与土地利用一体化模型将从“空间”模型向“时空间”模型转变;从“宏观”集计模型向“微观”行为模型转变;从“交通-土地”模型向“综合系统”模型转变;从“样本数据”模型向“大数据”模型转变;从“交通规划”应用向“国土空间规划”应用转变。旨在对城市交通与土地利用一体化模型的一般结构、原理类型、当前面临的问题和挑战进行深入剖析,以推进一体化模型在中国的研究与开发。     

广州市土地利用结构优化配置及其潜力

基于广州市土地利用结构、地区经济、生态与社会特点,结合广州城市总体规划(2010-2020),对广州市域土地利用结构进行生态经济效益-自然生态效益导向的多目标优化配置研究。结果表明：对广州市土地利用数量结构进行调整,一定时间内能够满足地区可持续发展;但是,土地仍然承担着巨大的压力,焦点是建设用地和农用地之间的数量转变,以耕地为主,也包括对未利用土地的利用;土地利用潜力度模型结果显示,通过优化土地利用结构,土地系统的协调度增大而潜力度减小：在生态经济效益目标下,潜力度分别为0.628～0.661,0.372～0.339;在自然生态效益目标下,分别为0.531～0.782,0.469～0.218,表明土地利用系统将趋于更高的协调状态,而其数量结构优化配置的潜力减小,优化各产业部门内部土地资源结构及其空间布局,将是广州市土地资源高效集约利用和进一步挖潜的主导方向。     

收缩情境下城市用地结构时空格局演变及动力机制——以中国东北地区为例

构建城市用地结构时空格局演变分析框架,引入信息熵和偏移份额模型定量剖析2000-2017年中国东北地区城市用地结构时空格局演变特征,通过计量经济模型分阶段对比分析收缩情境下东北地区城市用地结构演变的动力机制.结果表明:东北地区城市用地总面积持续增长,用地结构信息熵整体呈现先上升、后下降的趋势.2011年以后,城市用地结...     

中国中、东部典型样区不同发展水平下土壤与土地利用多样性的对比分析(英文)

Two typical provincial capitals (Nanjing and Zhengzhou) and two counties (Rugao and Yuanyang) in east (Jiangsu Province) and central (Henan Province) China were chosen respectively as the developed and less developed comparative cases for pedodiversity and land use diversity correlative analysis by borrowing the recently better developed pedodiversity methodology. Land use classification was worked out using remote sensing images in three different periods (1986-1988, 2000-2001 and 2004-2006) for these studied case areas before the calculation of the constituent diversity index and spatial distribution diversity index modified after Shannon entropy in 2 km×2 km grid scale of the soil and land use pattern were conducted and then a connection index was proposed to evaluate the relationship between soil and land use diversity. Results show that during the years from 1986 to 2006, the composition and spatial distribution of regional land use pattern had changed greatly. The agricultural land area of all the studied case areas decreased obviously in which Nanjing has the highest decrement of 895.98 km 2 mainly into urban use while the other land use type area changes show the same trend. The connection index of four typical soil family types and typical urban land use types, i.e., urban construction land, transportation land and industrial and mining area all increased in this period. In the studied case areas, there is the highest soil constituent diversity in Zhengzhou at 0.779 while the simplest soil constituent diversity in Rugao at 0.582. Meanwhile we have higher land use diversity in the more urbanized Jiangsu Province than Henan Province, Nanjing is ranking the first that has been getting higher and higher in the three periods at 0.366 in 1986-1988, 0.483 in 2000-2001 and 0.545 in 2004-2006. Finally, the connection index figures to evaluate the relationship between soil and land use diversity of the studied areas were compared to show the similar phenomenon that this figure grows fastest in Nanjing followed by Zhengzhou and other places.     

Measuring the temporal instability of land change using the Flow matrix

This article introduces the Flow matrix, which expresses the sizes of transitions among categories between two time points. We use the Flow matrix to create a metric R that measures the instability of annual change among time intervals that partition the time extent. Specifically, R is the proportion of change that would need to be reallocated to different time interval(s) to achieve uniform change during the time extent. This article computes R for 10 Long Term Ecological Research (LTER) sites and for seven case studies from published land change data. Of the 10 LTER sites analyzed, the Andrews site in Oregon had the highest R value (37.1% of change), while the Luquillo site in Puerto Rico had the lowest (1.7% of change). We analyze the mathematical behavior of R, especially with respect to how the partitioning of the time extent into intervals can influence R.     

唐山市中心区土地利用潜力评价研究

以唐山市中心区土地利用为例,在分析研究区土地利用现状的基础上,综合研究影响城市土地利用潜力的因素,设计了评价流程,构建了土地利用潜力评价的指标体系和评价模型。结果显示,中心区总面积11 479.27hm2,其中无容量土地、容量完全损失土地、未利用的土地、低度利用的土地、合理利用的土地、过度利用的土地6种潜力类型的土地面积分别占总面积的27.86%,6.43%,5.69%,23.64%,28.62%,7.76%,土地利用率偏低,有很大的挖潜空间。通过对唐山市中心区城市土地利用潜力的评价,不仅为中心区的城市建设和土地利用提供依据,也为城市土地利用潜力评价的推广和应用提供方法和思路。     

模拟区域土地利用变化的多主体模型及其在鄱阳湖区的应用(英文)

在较大区域内,城市用地扩张和农村土地利用发展一般同步进行。为了能够正确理解这类区域内土地利用/土地覆被变化的动力机制,本研究开发了一个基于多主体系统的土地利用模型。模型将鄱阳湖区视为一个典型区域,利用有限扩散聚合算法模拟城市主体的行为,采用基于涌现理论的主体模型模拟农村土地利用。农村子模型包括多种类型的主体和环境影响层,其中最重要的是农户主体,农户主体可以根据自身条件和周围的环境影响参数确定下一步土地利用的决策,政府主体则通过实施不同的政策对土地利用进行宏观控制。我们根据农户总收入和外出务工人员比例将农户主体分为6类,每种类型的农户主体遵循不同的决策规则。结果表明,本模型的模拟结果与鄱阳湖区1985 – 2005年的土地利用/土地覆被变化趋势高度相符。尤其重要的是,模拟结果充分表明农户主体的土地利用决策与国家政策紧密相关。这表明本研究建立的基于多主体的土地利用模型是一种评估政府土地管理政策效果的有效手段。     

公交导向型城市开发机理及模式构建

城市交通、土地利用和城市空间结构的相互关系一直是城市地理学、城市规划学和交通地理学研究的核心问题。大容量公共交通、混合型土地开发、宜人的步行环境和高质量的公交服务是公交导向型开发(TOD)模式的主要特点。本文分析了TOD对城市土地利用和城市空间结构的作用机理发现:土地开发强度、土地利用结构以及土地价值是TOD影响城市土地利用的3 个主要方面,而TOD对路网结构、城市规模和城市空间布局的作用进一步影响城市空间结构及其演化。进一步研究发现:大容量公共交通的建设使得城市竞租曲线和开发密度具有多波峰的特点,遵循"随着时间的变化从城市中心向外围递减"的规律。基于中外城市发展背景差异,本文从宏观、中观和微观3 个层面提出建立以大容量公共交通为主轴、以TOD站点为枢纽的轴辐网络状城市空间结构,并提出了TOD站点开发的距离、级差密度、多样性和设计的4Ds原则。