可动态定制的城镇土地定级系统开发技术研究

土地定级数据、指标(因素、因子)及指标计算方法在空间、时间上存在差异性,而传统的城镇土地定级系统伴随着具体的定级工作研发,具有量身定制的特点,缺乏对数据、指标(因素、因子)及指标计算方法时空动态演变的支持。基于组件的框架技术面向特定领域,提供了支持领域内变化性特征的处理机制,使软件具有了动态演化能力。本文从设计角度对框架模型及框架扩展机制进行了分析,在此基础上对土地定级进行业务建模,把业务中相对稳定部分实现为系统主框架,把易变的指标、方法、数据规整、抽象为扩展点,并提供了基于XML+多级菜单的易变特征动态配置、基于动态解析的易变特征动态加载方案,建立了支持时空演变的可动态定制的城镇土地定级系统。     

利用时空立方体挖掘小微地震时空演变模式

挖掘分析小微地震的时空演变模式，可为地震灾害的分析与预报提供辅助决策参考。本文以四川地区的地震监测数据为基础，利用时空立方体融合地震点的空间、时间与属性数据，基于时空热点统计分析方法挖掘小微地震的时空冷热点分布模式。试验结果表明：在试验数据的时域内，四川地区小微地震的热点模式主要表现为连续热点、逐渐减少热点和振荡热点。冷点模式主要是连续冷点，且冷点覆盖范围比热点覆盖范围广。基于时空立方体的时空热点分析方法能够发挥时空统计学的优势，可有效挖掘分析小微地震的时空演变趋势。     

Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: Evidence from the Phoenix metropolitan region

Urbanization is a natural and social process involving simultaneous changes to the Earth’s land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.     

A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques

This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and ?6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC.     

Incorporating GIS data into an agent-based model to support planning policy making for the development of creative industries

This paper presents an extension to the agent-based model “Creative Industries Development–Urban Spatial Structure Transformation” by incorporating GIS data. Three agent classes, creative firms, creative workers and urban government, are considered in the model, and the spatial environment represents a set of GIS data layers (i.e. road network, key housing areas, land use). With the goal to facilitate urban policy makers to draw up policies locally and optimise the land use assignment in order to support the development of creative industries, the improved model exhibited its capacity to assist the policy makers conducting experiments and simulating different policy scenarios to see the corresponding dynamics of the spatial distributions of creative firms and creative workers across time within a city/district. The spatiotemporal graphs and maps record the simulation results and can be used as a reference by the policy makers to adjust land use plans adaptively at different stages of the creative industries’ development process.     

Illegal land use change assessment using GIS and remote sensing to support sustainable land management strategies in Taiwan

Land use is changing at accelerated rates in Taiwan, and illegal land use change practices (ILP) are regularly observed within conservation areas. For this reason, we map high-potential areas of ILP within the Soil and water conservation zone (SWCZ) as an aid for effective land management and conducted an exploratory analysis of explanatory variables to evaluate their variability within ILP hot spots. We used variables relevant to hot spots to develop a logistic regression model and identified seven statistically significant variables. We re-applied the logistic regression approach to produce spatially explicit predictions of ILP. High probability areas are distributed along the coastal regions, covering 26% of the SWCZ, and their major drivers are related to accessibility and topography. The results from this research provide relevant information on the major drivers of ILP and high-potential areas, which can support officials in monitoring efforts for better planning and governance within the SWCZ.     

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations.     

地理国情在武汉市土地资源承载力评价中的应用

基于武汉市地理国情数据,围绕城市土地资源承载力问题,研究建立了一套城市土地资源承载力评价技术方法。首先,根据土地利用功能进行"三生用地空间"划分,将市、区有关经济统计指标按街道进行了尺度下推;其次,在分析评价适宜建设开发用地、已利用建设用地、可开发用地资源的基础上,开展了土地资源承载状态的综合评价;最后,利用地理加权回归分析方法,分析了人口、经济、交通因素对土地资源承载状态的影响。该方法将城市土地资源承载力评价由传统的人口承载力测算转换为开发建设状态评价,不仅为城市承载力评价提供了一种新的分析思路,也为制定精细化和差异化的城市建设用地资源配置和调控政策提供了科学依据。     

城市交通热点区域识别及空间交互分析

出租车载客数据可以用于研究居民的出行特征,提取城市的交通热点区域,但对城市交通热点区域的交互关系研究相对较少。本文以纽约市的出租车行程记录数据为数据源,利用交通小区划分结合出租车载客数据提取城市交通热点区域,基于复杂网络的方法对不同日期类型和天气情况的城市交通热点区域空间交互网络进行研究并进行社区发现。结果表明,热点区域受城市核心区的影响而聚集在核心区域周围,城市内社区的形成可以克服地形和行政区域等因素的影响。研究结论有望为城市规划、城市交通管理、出租车调度、以及人们的出行等提供信息参考。     

1973年—2013年经济特区城市空间扩展遥感监测

选取1973年—2013年多源遥感影像为数据源,以深圳、厦门、海口为例,开展中国经济特区城市建城区的长时序、高频数空间扩展遥感监测。选取城市扩展速度、城市扩展对土地利用的影响、城市形态紧凑度和城市重心迁移作为指标,剖析经济特区城市扩展的时空特征,耦合自然要素与人为要素,对比分析深圳、厦门和海口在城市扩展过程中表现出来的异同,并探讨其原因。结果表明:(1)近40年来,经济特区城市扩展先后经历了一个缓慢平稳扩展阶段、两个加速扩展阶段和两个减速扩展阶段。深圳的扩展速度最快,其次是厦门,海口的扩展速度最慢;(2)耕地对经济特区城市扩展贡献最大,林地、水域、城乡工矿和居民用地、海域也是经济特区城市扩展的重要土地来源,草地和未利用土地的贡献最小;(3)经济特区城市的空间形态总体趋于松散,1973年—2004年城市紧凑度剧烈降低,2004年之后趋于平稳;(4)在政策与填海造地工程的共同影响下,经济特区城市重心有向海岸线方向迁移的趋势,深圳的迁移量最大,海口最小。     

成渝城市群城市建设用地扩张格局及规模体系分析

基于1990—2018年六期城市建设用地监测数据，本文利用城市用地年面积增长率指数、公共边测度、核密度估计、位序-规模法则等方法，刻画成渝城市群扩张演化特征及其发展规律，并对城市群规模体系进行分析。结果表明：成渝城市群城市建设用地扩张呈先快后慢的趋势，空间上呈“中部高速，南北低速”的特点，建设用地扩张密度高值主要集中在成都、重庆二市且城市群内部连片发展不明显。城市群新增城市用地呈现无序蔓延态势，并未趋于紧凑。2000—2010年，成渝城市群规模分布极化，呈现明显的双核首位特征，2010年后规模分布由非均衡趋于均衡。     

土地市场动态监测与监管系统设计思路

通过分析土地市场各类业务,研究了土地市场动态监测监管体系,提出了建立全流程、全口径监管思路。从提高数据质量、保障数据完整、接受公众监督、捕捉社会热点等角度,设计了实现监管思路的各项系统功能。     

Spatiotemporal multiscale diagnosis model to proactively respond to the multi-country monkeypox virus outbreak in 2022

This research analyzes the spatiotemporal trend of 23,121 monkeypox virus cases in the multi-country outbreak that affected 82 countries from January 2022 to July 2022. The spatiotemporal trends analysis is developed using open data and GIS to model 3D bins and emerging hot spots globally (data by country) and nationally (data by region) for hardest hit countries, like the USA and Spain. The implemented methodology distinguishes between problem areas —as significant hot spots— and countries with no pattern. Results show consecutive hot spot patterns in Western Europe and high location quotients in North America. Factually, the countries with consecutive patterns record 16,494 cases, that is, 71.34% of the cases, where 7.63% of the world population live. At the national level, in the analysis of the USA and Spain, the results reveal regional differences with significative hot spots in California and on the East Coast of the USA and the Mediterranean coast of Spain. The proposed methodology facilitates the monitoring of the spatiotemporal evolution of monkeypox cases and is scalable and replicable using non-arbitrary and statistical parameters. The findings indicate problematic zones in real-time, enabling policymakers to develop focused interventions and proactive strategies to mitigate the future risk of monkeypox.     

An operational framework for object-based land use classification of heterogeneous rural landscapes

The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic landscapes. It does this by using an image stack of RGB-NIR, Normalised Difference Vegetation Index (NDVI) and textural bands in a two-phase object-based classification. The framework can be applied to data acquired by different sensors, with different view and illumination geometries, at different times of the year. Even with these complex input data the framework can produce classification results that are comparable across time. Here we describe the framework and present an example of its application using data from QuickBird (2 images) and GeoEye (1 image) sensors.     

顾及属性特征的城市设施热点识别方法

研究城市设施的热点分布对把握当前城市形态具有重要意义。传统的设施热点识别方法容易忽略设施的特征尺度且多以区域识别为主,缺少精准化提取设施热点的方法体系。针对上述问题,本文提出了一种顾及属性特征的设施热点识别方法,并以北京市住宅设施为例进行了试验分析。首先将设施的属性值作为权重,进行加权核密度估计生成密度值表面,利用极值点探测模型提取极值点;然后采用Getis-Ord G_i^*统计进行空间自相关分析,生成具有显著统计学意义的热点区域,筛选极值点得到热点。结果表明,该方法能够准确有效地识别设施热点并进行合理的等级划分,为城市设施空间布局研究提供多样化视角。     

Nighttime light data-based analysis of spatiotemporal patterns evolution and driving forces of economic development in China at prefecture level (1992–2020)

Intercity lighting data are an important resource for studying spatial and temporal patterns in regional urban development as an indicator of the intensity of urban social and economic activity. Understanding the evolutionary characteristics of the spatial pattern of regional economic development can support decision-making in regional economic coordination and sustainable development strategies. Based on a long time series of nighttime lighting data from 1992 to 2020, this study used the Theil index, Markoff transfer matrix, spatial autocorrelation, and spatial regression to analyze spatiotemporal evolutionary characteristics and drivers of urban economic development in China. The study found that from 1992 to 2020, China's economic development hot spots have been concentrated in highly developed urban agglomerations namely the Beijing–Tianjin–Hebei region, Shandong Peninsula, Yangtze River Delta, and Pearl River Delta. Cold spots were mainly concentrated in the central-west and southwest of the country. The economic growth rate shows an opposite spatial pattern, which demonstrates the effectiveness of the national coordinated development strategy for regions. The Theil index for urban economic development in China shows an overall downward trend, and the overall economic disparity is mainly due to the relatively low economic development of Tibet, Xinjiang, Gansu, and other western provinces. Therefore, regional economic development remains significantly uneven. In China, the economic type of cities is relatively stable, and the probability of leapfrogging types is low; however, the level of cities with high resource dependence or a single economic structure easily downgrades. The level of economic development and the related socioeconomic factors of neighboring cities influence an obvious spatial spillover effect in the development of urban economies in China. The pattern of China's urban economic development is mainly affected by innovation capacity, financial support, capital investment, transportation infrastructure, and industrial structure.     

Voronoi图在城镇地价指数监测中的应用探讨

城市地价指数是地价动态监测的重要手段。针对我国城市地价指数测算公式发表了自己的见解,并提出采用Voronoi图计算地价监测点的影响范围,作为监测样点的权重用于计算地价指数的方法,以广东省某市的地价指数测算为例证明了该方法的可行性。     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.