Comparison Between Four Methods for Data Fusion of ETM+ Multispectral and Pan Images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.

An Open Source Toolkit for Identifying Comparative Space-time Research Questions

Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.

Bridging open source tools and Geoportals for interactive spatial data analytics

Geoportals have been the primary source of spatial information to researchers in diverse fields.Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals.Researchers could use the Geoportal to conduct basic analysis without offline processing.In practice,domain-specific analysis often requires researchers to integrate heterogeneous data sources,leverage new statistical models,or build their own customized models.These tasks are increasingly being tackled with open source tools in programming languages such as Python or R.However,it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis.This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting.The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies.A python package is provided to manipulate this platform in the local programming environment.The server side of the Geoportal implements a set of service endpoints that allows the package to upload,transform,and process user data and seamlessly integrate them into the existing datasets.A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data.This work attempts a new direction in Geoportal development,which could further promote the transformation of Geoportals into online analytical workbenches.