Thematic maps for land consolidation planning in Hubei Province, China

Wang

et al. 2016

Transactions in GIS

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Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non-spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC1) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC1 algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC1 algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers. K E Y W O R D S adaptive dual clustering, data mining, Delaunay triangulation, hierarchical structure, rural settlement zoning 1 | I NTR OD U CTI ON Clustering algorithms aim to separate a dataset to minimize the within-group variance and maximize the betweengroup variance. As such, they are a powerful tool applied in various applications, such as in the unsupervised classification of image processing, hot spot analysis, and outlier detection. Many traditional clustering algorithms have been reported in the literature and can be grouped into six categories, i.e. density-based (Andrade et al.

Section: Resultsmentioning

confidence: 99%

An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning

Wang

et al. 2016

Transactions in GIS

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“…While data on agricultural production and area under cultivation are disaggregated to some extent in government statistics (city-wide or district-wise and by certain crops or categories of crop), the actual spatial distribution of different types of agriculture is very difficult to determine, especially in highly heterogeneous, rapidly changing peri-urban contexts. Ecosystem service mapping methods typically use only one category for agricultural land (Costanza et al 2014(Costanza et al , 1997Jiang 2017;Long et al 2014;Wu et al 2013;Xie et al 2017;Zhou et al 2014) and Wuhan's master plan and provincial land-use maps do not distinguish different cropping systems (Kong et al 2014;Zheng et al 2018). This makes it impossible to interpret the implications of displacing different groups engaged in different types of agricultural activity (e.g.…”

Section: Challenges To Disaggregating Sdg Trade-offs In Peri-urban Lamentioning

confidence: 99%

Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: a tool to support inclusive urban planning

et al. 2020

Transitional peri-urban contexts are frontiers for sustainable development where land-use change involves negotiation and contestation between diverse interest groups. Multiple, complex trade-offs between outcomes emerge which have both negative and positive impacts on progress towards achieving Sustainable Development Goals (SDGs). These trade-offs are often overlooked in policy and planning processes which depend on top-down expert perspectives and rely on course grain aggregate data which does not reflect complex peri-urban dynamics or the rapid pace of change. Tools are required to address this gap, integrate data from diverse perspectives and inform more inclusive planning processes. In this paper, we draw on a reinterpretation of empirical data concerned with land-use change and multiple dimensions of food security from the city of Wuhan in China to illustrate some of the complex trade-offs between SDG goals that tend to be overlooked with current planning approaches. We then describe the development of an interactive web-based tool that implements deep learning methods for fine-grained land-use classification of high-resolution remote sensing imagery and integrates this with a flexible method for rapid trade-off analysis of land-use change scenarios. The development and potential use of the tool are illustrated using data from the Wuhan case study example. This tool has the potential to support participatory planning processes by providing a platform for multiple stakeholders to explore the implications of planning decisions and land-use policies. Used alongside other planning, engagement and ecosystem service mapping tools it can help to reveal invisible trade-offs and foreground the perspectives of diverse stakeholders. This is vital for building approaches which recognise how trade-offs between the achievement of SDGs can be influenced by development interventions.

“…Remote Sensing (RS) and Geographic Information Systems (GIS) techniques offer effective tools for examining, updating and retrieval of the land use dynamics of the region as well as for monitoring, mapping and management of natural resources in less time, at low cost and with improved accuracy (Star et al, 1997;McCracker et al,1998;Chilar, 2000). By understanding the driving forces of land use changes in the past, managing the recent situation with contemporary GIS tools, and modeling the forthcoming changes, one is able to develop plans for numerous uses of natural resources for sustainable development and natural management (Smiraglia et al, 2013;Jame and Jaun, 2014;Xuesong et al, 2014).…”

Section: Land Use Land Cover Mappingmentioning

confidence: 99%

Geospatial mapping of Srinagar City

Murtaza¹,

Rather²,

Lateef³

et al. 2015

J. Geogr. Reg. Plann.

Mapping has remarkably become an essential tool to represent the land surface processes, utility sites and the interactions of the human activities with the environment. The rapid evolution of geographic information data management and the automation of satellite image interpretation through remote sensing techniques are providing increasingly abundant ways of mapping. Geospatial approach is very useful tool to demonstrate the spatial disparity of land use land cover and the distribution of key areas, Heritage sites, and Tourist utilities. In this paper, an integrated approach of remote sensing, geographic information, GPS and field surveys was utilised to map the Srinagar city. Mapping was sub divided in three broad categories: land use land cover dynamics mapping, tourist utility mapping and cultural heritage mapping. To improve the adverse environmental impacts of urban expansion, planning regulations need to be enforced and effective coordination should be ensured to save the declining natural resource base for sustainable development and tourism.