A land-use mix allocation model considering adjacency,intensity, and proximity

ABSTRACT

Land-Use Mix (LUM) refers to the strategy of integrating complementary functions within a building or area. While LUM has become a dominant approach in urban planning, its actual benefits and vision for spatial planning remain unclear. To clarify this issue, this study discerns the spatial features of land-use patterns depending on the compatibilities among land-use categories. Accordingly, this study introduces three LUM measures – adjacency, intensity, and proximity – to identify differences in the spatial distribution of land-use categories. Based on these measures, a land-use allocation model is developed to specify spatial patterns satisfying the given compatibilities. This model is tested by applying the concept of the neighborhood unit on a case study of normative land-use patterns subject to specified compatibilities. The results describe spatial features of four compatibility sets, including a set exhibiting a compatibility conflict between the same land-use pair and LUM measures when, for example, a given land-use pair is compatible in terms of intensity but incompatible in terms of proximity. Understanding the spatial features of a normative land-use pattern that satisfies various possible compatibilities will facilitate the incorporation of the LUM approach into local planning guidance and zoning ordinances.     

土地利用动态模拟模型的构建及其应用——以北京市海淀区为例

CLUE-S模型是一个基于经验统计原理的模拟多土地利用类型空间变化的动态模拟模型,它在世界多个国家和地区的区域尺度农业、森林为主的土地利用变化模拟中得到应用。作者对CLUE-S模型进行了改进,改进后的模型 (CLUE-SII) 引入了动态计算的邻域分析因子,可以对土地利用变化中的自发过程、自组织过程和土地利用类型间的竞争进行模拟,还可以根据研究区域特点构建不同的模拟方案,在这些模拟方案中,局地因子和邻域因子在土地利用变化中的作用方式不同。应用CLUE-SII对北京市海淀区1991~2001年土地利用变化进行了多方案模拟,结果表明邻域因子对城镇用地变化具有重要作用,其中将邻域因子看作自发过程放大因子的模拟方案获得了较好的模拟结果,整图符合比达到77%,其中城镇用地符合比达到82%,Kappa值达到0.754。CLUE-SII在北京市海淀区的应用实例表明,该模型可以对高分辨率和多土地利用类型下的城市扩展进行有效模拟,扩展了CLUE-S的应用领域;通过构建多模拟方案,不但可以探索最佳的模拟结果,还可以研究和分析不同土地利用驱动因子在土地利用变化中的作用模式。     

城市土地利用变化中邻域相互作用的特征——日本三大都市圈的比较研究

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan,i.e. Tokyo,Osaka,and Nagoya,were compared using such aids as the neighborhood interaction model and similarity measure function. As a result,urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories,meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas,which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

Geostatistical modelling of spatial uncertainty using p -field simulation with conditional probability fields

This paper presents a variant of p-field simulation that allows generation of spatial realizations through sampling of a set of conditional probability distribution functions (ccdf) by sets of probability values, called p-fields. Whereas in the common implementation of the algorithm the p-fields are nonconditional realizations of random functions with uniform marginal distributions, they are here conditional to 0.5 probability values at data locations, which entails a preferential sampling of the central part of the ccdf around these locations. The approach is illustrated using a randomly sampled (200 observations of the NIR channel) SPOT scene of a semi-deciduous tropical forest. Results indicate that the use of conditional probability fields improves the reproduction of statistics such as histogram and semivariogram, while yielding more accurate predictions of reflectance values than the common p-field implementation or the more CPU-intensive sequential indicator simulation. Pixel values are then classified as forest or savannah depending on whether the simulated reflectance value exceeds a given threshold value. In this case study, the proposed approach leads to a more precise and accurate prediction of the size of contiguous areas covered by savannah than the two other simulation algorithms.     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

Combining spatial transition probabilities for stochastic simulation of categorical fields

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.     

A Methodology for the Assessment of Unconventional (Continuous) Resources with an Application to the Greater Natural Buttes Gas Field,Utah

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.     

Forthcoming events

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.     

基于优化GeoCA模型的土壤侵蚀时空变化模拟——以福建省长汀县为例

土壤侵蚀动态监测是侵蚀研究体系中的重要内容, 目前研究主要集中在强度评价以及趋势预测两方面, 其中, 地理元胞自动机(Geo-Cellular Automata, GeoCA)是最常用的土壤侵蚀趋势模拟模型, 但是现有研究主要基于邻域计算规则, 强度模型的数学意义, 而忽视了元胞自身发生变化的可能性, 不能完全体现土壤侵蚀演变的复杂性, 这些问题不仅降低了模拟的精度, 而且使得趋势预测与侵蚀评价两者间的融合性降低, 不利于完整的研究体系形成。本研究认为土壤侵蚀时空变化趋势是其自身侵蚀状态、自然条件以及邻域转换规则共同作用决定, 从而设计元胞侵蚀强度指数算法、元胞侵蚀强度函数以及元胞邻域转换函数, 对传统GeoCA进行了优化, 并在GIS与遥感技术支撑下, 以福建省长汀县为研究区, 设计了上述算法的整合模型, 对福建省长汀县30 年来土壤侵蚀时空演替过程进行了测算与分析。研究结果显示: 长汀县土壤侵蚀最严重的地区主要分布在以河田镇为中心的汀中地区, 20 世纪90 年代以来, 土壤侵蚀治理工作成效显著, 改善趋势明显加快, 至2000 年以后有所放缓。预计至2020 年, 土壤侵蚀区的比重将从1990 年的约40%下降至约20%。通过比较, 整合算法的精度达到72.7%, 高于单纯的侵蚀强度评价算法以及传统GeoCA模型, 证明优化GeoCA不仅是进行土壤侵蚀时空演变模拟研究的有效手段, 更为土壤侵蚀这一复杂地理系统的元胞表述规则研究进一步深入提供了思路。     

Characteristics of neighborhood interaction in urban land-use changes: A comparative study between three metropolitan areas of Japan

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore, it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan, i.e. Tokyo, Osaka, and Nagoya, were compared using such aids as the neighborhood interaction model and similarity measure function. As a result, urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories, meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas, which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

Neighborhood size and spatial scale in raster-based slope calculations

Raster-based slope estimation is routine in GIS. Like many other terrain attributes, the slope at a location is determined from elevations of surrounding cells. This spatial extent – ‘neighborhood size’ – is often treated as the ‘spatial scale’ of the calculation. In fact, neighborhood size and spatial scale are two connected yet different concepts, but few studies have investigated the relationship between them. The distinction is important because neighborhood size is under user control whereas spatial scale is merely implicit in the computational method. This article attempts to clarify and provide a more precise meaning of the two terms by considering slope operators from the standpoint of the frequency (or wavenumber) domain. This article derives analytical expressions for the amplitude response functions of four popular slope estimators. These are used to characterize the individual methods and also to show that the neighborhood size and spatial scale of a slope calculation are not numerically the same. In fact, because there is no single spatial scale that can be unambiguously associated with a given neighborhood size, neighborhood size cannot be an adequate indicator of spatial scale. Furthermore, this article shows that different indices of ‘scale’ yield different impressions about the action of a slope estimator and its response to changing neighborhood size. Therefore, it is necessary to examine the amplitude response function when investigating the spatial scale. The article also provides guidance for GIS practitioners when selecting a slope estimation method.     

CA-Markov模型的空间尺度敏感性研究

以广州市花都区为研究区,研究利用CA-Markov模型进行土地利用变化模拟的空间尺度敏感性特征,结论如下:①元胞尺寸的选择会明显影响模拟结果,元胞尺寸越大,模拟结果精度越低。模型中存在元胞尺寸的阈值,当元胞尺寸超出该阈值时,模拟结果的精度急剧下降,因此对于元胞尺寸的选择必须要慎重。②邻域类型的选择也会对模拟结果产生影响。采用3×3冯诺依曼邻域的模拟结果会比3×3摩尔邻域和5×5摩尔邻域生成更多的斑块数量和更高的斑块密度,但是模拟结果的Kappa系数值相差不大。     

Neighborhood-scale urban form,travel behavior,and CO2 emissions in Beijing: implications for low-carbon urban planning

With an increasing awareness of global climate change, the effect of urban spatial organization, at both city and neighborhood scales, on urban CO₂ emission reduction has attracted much scholarly and practical attention. Using Beijing as a case study, this article examines the extent to which neighborhood-scale urban form may contribute to reduction of travel-related CO₂ emissions in the context of rapid urbanization and spatial transformation. We derive complete travel-activity records of 1,048 residents from an activity diary survey conducted in 2007. Analysis using structural equation models finds that residents living in a neighborhood with higher land use mix, public transit accessibility, and more pedestrian-friendly street design tend to travel in a “low-carbon” manner and emit less CO₂ in daily travel, even controlling for residential and travel preferences. This article offers empirical evidence that sheds light on debates about policy measures to facilitate China’s transition toward sustainable and low-carbon urban development.     

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

     

Multistop-based measurements of accessibility in a GTS environment

Abstract

The opportunities available at a demand location are usually measured as the costs of reaching a specified critical number of facilities from that location. This method does not however, account for multistop trips nor for differences in the diversity of supply at the level of individual facilities. In this paper we introduce an alternative measurement method that overcomes these shortcomings. In this method the probability of successfully visiting a specific facility is assumed to be a function of the diversity of supply provided. Trip routes are constructed that have an acceptable probability of success. Then, the expected costs of travelling the optimum route are determined as an indicator of spatial opportunities. The proposed method has been implemented in a GIS environment, using typical GIS data and GIS tools for spatial analysis and display. The results of a case study indicate that the new method, compared to current methods, may lead to different evaluations of the level of opportunities at demand locations.     

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.     

Multiple Point Metrics to Assess Categorical Variable Models

Geostatistical models should be checked to ensure consistency with conditioning data and statistical inputs. These are minimum acceptance criteria. Often the first and second-order statistics such as the histogram and variogram of simulated geological realizations are compared to the input parameters to check the reasonableness of the simulation implementation. Assessing the reproduction of statistics beyond second-order is often not considered because the “correct” higher order statistics are rarely known. With multiple point simulation (MPS) geostatistical methods, practitioners are now explicitly modeling higher-order statistics taken from a training image (TI). This article explores methods for extending minimum acceptance criteria to multiple point statistical comparisons between geostatistical realizations made with MPS algorithms and the associated TI. The intent is to assess how well the geostatistical models have reproduced the input statistics of the TI; akin to assessing the histogram and variogram reproduction in traditional semivariogram-based geostatistics. A number of metrics are presented to compare the input multiple point statistics of the TI with the statistics of the geostatistical realizations. These metrics are (1) first and second-order statistics, (2) trends, (3) the multiscale histogram, (4) the multiple point density function, and (5) the missing bins in the multiple point density function. A case study using MPS realizations is presented to demonstrate the proposed metrics; however, the metrics are not limited to specific MPS realizations. Comparisons could be made between any reference numerical analogue model and any simulated categorical variable model.     

地理元胞自动机模型研究进展

元胞自动机(Cellular Automata,简称CA)是一种基于微观个体的相互作用空间离散动态模型,其强大的计算功能、固有的平行计算能力、高度动态及空间概念等特征,使它在模拟空间复杂系统的时空动态演变研究具有较强的优势。文章回顾了元胞自动机的发展历程,阐述了CA在地理学中的主要应用领域和研究进展,在此基础上,以现实世界地理实体及现代城市扩张特征为视角,分析目前CA研究所面临的问题,并对其未来的研究趋势进行了初步探讨,认为以下3个方面将是未来CA研究的热点：① 利用不规则元胞及可控邻域的CA模型,对不同规则或不同邻域地理实体的模拟研究;② 采用三维元胞自动机对现代城市扩张进行立体化模拟,以克服二维CA模型的缺陷;③ 将矢量元胞自动机模型应用于地理实体的模拟研究,进一步提高模拟精度。