scholarly journals The importance of green amenities for small creative actors in Tokyo: Comparing natural and sociocultural spatial attraction characteristics

2019 ◽  
Vol 59 (1) ◽  
pp. 159 ◽  
Author(s):  
Matjaž Uršič ◽  
Kazushi Tamano
Keyword(s):  
Spatial Attraction

Large-scale subpixel mapping of landcover from MODIS imagery using the improved spatial attraction model

2018 ◽  
Vol 12 (04) ◽  
pp. 1 ◽  
Author(s):  
Lizhen Lu ◽  
YanLin Huang ◽  
Liping Di ◽  
Danwei Hang
Keyword(s):  
Large Scale ◽  
Modis Imagery ◽  
Attraction Model ◽  
Subpixel Mapping ◽  
Spatial Attraction

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

2006 ◽  
Vol 27 (15) ◽  
pp. 3293-3310 ◽  
Author(s):  
Koen C. Mertens ◽  
Bernard de Baets ◽  
Lieven P. C. Verbeke ◽  
Robert R. de Wulf
Keyword(s):  
Mapping Algorithm ◽  
Pixel Mapping ◽  
Spatial Attraction

scholarly journals Location-based density and diversity – adding attraction variables to space syntax

2017 ◽  
Author(s):  
Lars Marcus ◽  
Meta Berghauser Pont ◽  
Gianna Stavroulaki ◽  
Jane Bobkova
Keyword(s):  
Space Syntax ◽  
Spatial Form ◽  
Street Network ◽  
Syntax Analysis ◽  
Architectural Model ◽  
Density Analysis ◽  
Important Exception ◽  
New Methodologies ◽  
The City ◽  
Spatial Attraction

The central variables in any urban model are distance and attraction (Wilson 2000). Space syntax research has contributed to the development of new geometric descriptions and measures of distance that have proven successful when it comes to capturing pedestrian movement. However, the description and measurement of attractions has not been central to the field. An important exception is the development of Place Syntax analysis, which concerns new methodologies and software that opens for analysis not only of different kinds of accessibilities in the street network in itself, but also analysis of the accessibility within the network to different forms of attractions, for instance, residents or retail (Ståhle et al 2005).   Place Syntax analysis is a generic form of analysis, why we may choose to analyse the accessibility to particular socio-economic attractions, but we may also conceive of a model of ‘pure’ spatial form – a kind of architectural model of the city. For instance, Place Syntax analysis has been applied in different kinds of density analysis, transforming density measures from area-based measures to location-based measures (Ståhle et al 2005). In this paper, we extend such spatial attraction to not only include the variable of density but also diversity and present results from an extensive empirical study including four European cities, paving the way towards a more complete architectural model of the city including both the analysis of distance and attractions.  

Exploring some issues of sub-pixel mapping based on directly spatial attraction

2010 ◽  
Author(s):  
Jian Zhang ◽  
Xiaoling Chen ◽  
WenXia Gan ◽  
Shoujin Yin ◽  
Hao Wu
Keyword(s):  
Pixel Mapping ◽  
Spatial Attraction

scholarly journals Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique

2019 ◽  
Vol 11 (3) ◽  
pp. 247 ◽  
Author(s):  
Peng Wang ◽  
Gong Zhang ◽  
Siyuan Hao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Fine Fraction ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
The Novel ◽  
Attraction Model ◽  
Resolution Mapping ◽  
Super Resolution Mapping ◽  
Fraction Images ◽  
Spatial Attraction

The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial–spectral information from the original image. To utilize more spatial–spectral information, improving remote sensing image super-resolution mapping based on the spatial attraction model by utilizing the pansharpening technique (SRMSAM-PAN) is proposed. In SRMSAM-PAN, a novel processing path, named the pansharpening path, is added to the existing SRMSAM. The original coarse remote sensing image is first fused with the high-resolution panchromatic image from the same area by the pansharpening technique in the novel pansharpening path, and the improved image is unmixed to obtain the novel fine-fraction images. The novel fine-fraction images from the pansharpening path and the existing fine-fraction images from the existing path are then integrated to produce finer-fraction images with more spatial–spectral information. Finally, the values predicted from the finer-fraction images are utilized to allocate class labels to all subpixels, to achieve the final mapping result. Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods.

scholarly journals Spatial attraction in migrants' settlement patterns in the city of Catania

2016 ◽  
Vol 35 ◽  
pp. 117-138 ◽  
Author(s):  
Angelo Mazza ◽  
Antonio Punzo
Keyword(s):  
Settlement Patterns ◽  
The City ◽  
Spatial Attraction

scholarly journals A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

2017 ◽  
Vol 9 (4) ◽  
pp. 360 ◽  
Author(s):  
Lizhen Lu ◽  
Yanlin Huang ◽  
Liping Di ◽  
Danwei Hang
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Attraction Model ◽  
Spatial Attraction
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