gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets

Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local spatial relations be-tween multiple variables in high dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted correlation and partial correlation in large multivariate datasets. gwpcorMap-per facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. We built gwpcorMapper using the R Shiny framework. The software inherits its core algorithm from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to explore census data in order to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection and parameter tuning for geographically weighted statistics. gwpcorMapper highlights that there are strong statistically clear local variations in the relationship between the number of commuters and the total number of hours worked when considering the total population in each district across the 23 special wards of Tokyo. Our application demonstrates that the ESDA process with high-dimensional geospatial data using gwpcorMapper has applications across multiple fields.Comment: 18 pages, 8 figures, 2 table

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches

Improving land cover classification using input variables derived from a geographically weighted principal components analysis

This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested

Investigating spatial error structures in continuous raster data

The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data

Spatial heterogeneity of errors in land cover data (Theory of Biomathematics and Its Applications XVI -Toward quantitative understanding for life Sciences-)

Thematic Land cover (LC) maps attempt to describe the Earth's terrestrial surface, encompassing all attributes of the biosphere (International Panel on Climate Change, 2000). LC has been regarded as an important component of the Earth system which physically interacts with climate, topography, human impacts, and their complex interactions. As LC maps are required to cover an area widely from local to global scales, remotely sensed (RS) imagery is often used, that is classified into defined thematic land cover classes by a classification method such as statistical and machine learning models. It is hence important to make an accurate LC classification map for high-quality quantification of the Earth system component. To assess the accuracy of the thematic LC classification map, conventional summary measures of error, such as user's, producer's, and overall accuracies for per-pixel classification, and mean signed deviation (msd), mean absolute error (mae), root mean square error (rmse) and Pearson's correlation coefficient (r) for sub-pixel classification. However, these summary measures of error do not take any spatial information (e.g., spatial heterogeneity) of error into account (Foody, 2005, 2002). A spatially explicit approach for the assessment is helpful to identify spatial characteristics of errors. This study demonstrates one of the spatial measures of error for assessing thematic LC maps. In this paper, a map for forest aboveground biomass (AGB) in the Yucatan peninsula, Mexico, estimated by Rodríguez-Veiga et al. (2016), is assessed

モンゴル国ウランバートルにおける時系列衛星画像を用いた都市域拡大とその環境影響に関する考察

京都大学0048新制・論文博士博士(地球環境学)乙第12828号論地環博第8号新制||地環||24(附属図書館)31315京都大学大学院地球環境学舎地球環境学専攻(主査)准教授 西前 出, 教授 渡邉 紹裕, 教授 小方 登学位規則第4条第2項該当Doctor of Global Environmental StudiesKyoto UniversityDFA

Geographically weighted partial correlation for spatial analysis

Spatial correlation between variables may exist if the observed data exhibits spatial variation in a manner that is described by Tobler's first law of geography. Partial correlation is useful when considering multivariate data as it can highlight the effects of certain control variables on the correlation between any two other variables. Techniques for estimating spatial correlation have been developed based on a geographically weighted scheme. However, a partial correlation technique for spatial data has not yet been considered. Hence, we describe a technique for obtaining geographically weighted partial correlation coefficients between three variables. This approach is then applied, as an example, to global climate data in order to explore the relationship between terrestrial vegetation (by NDVI proxy), land surface temperature, and precipitation in the year 2014. Spatial variations of those variables are observed and the geographically weighted correlation and partial correlation coefficients (along with associated levels of statistical significance) are compared

Mapping Fragmented Impervious Surface Areas Overlooked by Global Land-Cover Products in the Liping County, Guizhou Province, China

Imperviousness is an important indicator for monitoring urbanization and environmental changes, and is evaluated widely in urban areas, but not in rural areas. An accurate impervious surface area (ISA) map in rural areas is essential to achieve environmental conservation and sustainable rural development. Global land-cover products such as MODIS MCD12Q1, ESA CCI-LC, and Global Urban Land are common resources for environmental practitioners to collect land-cover information including ISAs. However, global products tend to focus on large ISA agglomerations and may not identify fragmented ISA extents in less populated regions. Land-use planners and practitioners have to map ISAs if it is difficult to obtain such spatially explicit information from local governments. A common and consistent approach for rural ISA mapping is yet to be established. A case study of the Liping County, a typical rural region in southwest China, was undertaken with the objectives of assessing the global land-cover products in the context of rural ISA mapping and proposing a simple and feasible approach for the mapping. This approach was developed using Landsat 8 imagery and by applying a random forests classifier. An appropriate number of training samples were distributed to towns or villages across all townships in the study area for classification. The results demonstrate that the global land-cover products identified major ISA agglomerations, specifically at the county seat; however, other fragmented ISAs over the study area were overlooked. In contrast, the map created using the developed approach inferred ISAs across all townships with an overall accuracy of 91%. A large amount of training samples together with geographic information of towns or villages is the key suggestion to identify and map ISAs in rural areas