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

Non fire-adapted dry forest of Northwestern Madagascar: Escalating and devastating trends revealed by Landsat timeseries and GEDI lidar data.

Ankarafantsika National Park (ANP), the last significant remnant of Northwestern Madagascar's tropical dry forests, is facing rapid degradation due to increased incidences of fire. This poses severe threats to biodiversity, local livelihoods, and vital ecosystem services. Our study, conducted on 3,052-ha of ANP's pristine forests, employed advanced remote-sensing techniques to assess fire impacts during the past 37 years. Our aims were to understand historical fire patterns and evaluate forest recovery and susceptibility to repeated fires following initial burns. Using data from multiple Landsat satellite sensors, we constructed a time series of fire events since 1985, which revealed no fire activity before 2014. The Global Ecosystem Dynamics Investigation (GEDI) lidar sensor data were used to observe forest structure in both post-fire areas and undisturbed zones for comparison. We recorded six fire incidents from 2014-2021, during which the fire-affected area exponentially grew. A significant fire incident in October 2021 impacted 1,052 hectares, 59% of which had experienced at least one fire in two-to-four years prior, with 60% experiencing two preceding incidents: one in 2017 and another in 2019. The initial fire drastically reduced plant cover and tree height, with subsequent fires causing minor additional loss. Post-fire recovery was negligible within the initial four years, even in patches without recurrent fires. The likelihood for an initial burn to trigger subsequent fires within a few years was high, leading to larger, more severe fires. We conclude that ANP's dry forests exhibit high vulnerability and low resilience to anthropogenic fires. Prompt preventive measures are essential to halt further fire spread and conserve the park's unique and invaluable biodiversity

Linear mixed models testing for the effects of the number of fires (n_fires) and the number of months since the last fire (n_months) on three attributes of forest structure: Plant area index, canopy cover, and canopy height.

Also included in the model are two random variables: the park’s management zone (community, buffer, or service) and season (dry season May-October vs. wet season November-April). (DOCX)</p

Study area in Northwestern Madagascar.

Maps and satellite imagery showing: (A) Precipitation patterns in Madagascar as defined by the number of months with rainfall less than 60mm (precipitation data is from [33]) and forested area as of 2022 (following the method of [5]); (B) enlarged panel of Ankarafantsika National Park; and (C) the study area on January 26, 2014 and (D) on December 15, 2021. Images in (C) and (D) are Landsat-8 images courtesy of the U.S. Geological Survey.</p

Typical conditions of vegetation in the study area after the fire in October 2021.

(A) Non-burnt and once burnt area at left and right of an ecotourism trail that served as a firebreak, (B) Boundary of twice burnt area (front) and once burnt area (back), (C) Unburned dead wood that will become fuel for the next fire, (D) Third burnt area with bare white sand.</p

Differences in forest structure across varying burn histories.

Differences in forest structure as described by plant area index (A), canopy cover (B), and canopy height (C), across the number of fire occurrences; and over months since the last fire (D-F), with ‘n’ denoting the number of data points from the Global Ecosystem Dynamics Investigation (GEDI) lidar sensor observed per factor. Groups in subplots A, B, and C not sharing lowercase letters are significantly different each other (p < 0.05) based on Dunn’s post hoc tests.</p

Fire history within the 3052-ha study area of Ankarafantsika National Park.

(A) The number of times fire has occurred (one, two, and three times indicated by color; Landsat-8 images courtesy of the U.S. Geological Survey); (B) the total area burnt for each type of burn history for each year; (C) burn severity as measured by the maximum detected differenced normalized burn ratio (max dNBR) in relation to the total number of fires experienced with ‘n’ being the number of pixels observed for each burn count.</p

Kruskal-Wallis (chi-statistic and p-value) and Dunn tests (p-values) on plant area index, canopy cover, and canopy height across varying numbers of forest fires.

Kruskal-Wallis (chi-statistic and p-value) and Dunn tests (p-values) on plant area index, canopy cover, and canopy height across varying numbers of forest fires.</p