GEO COLLECTOR BOT: A TELEGRAM-BASED OPEN TOOLKIT TO SUPPORT FIELD DATA COLLECTION

Georeferenced field data collection has become a popular practice allowing everyone to contribute to mapping objects or reporting events. The spread of mobile devices - capable of recording and sharing location coordinates, media and features while on the go - is primarily accountable for such diffusion. Accordingly, a number of mobile apps and software frameworks have been developed and released to perform field data collection. These frameworks allow to customize and dispatch collection forms as well as to manage contributors and records through web interfaces or database management systems. From the contributors’ perspective, specific mobile client apps need to be installed to access selectively the collection forms and contribute to the data collection on the field using their mobile devices. This operation might inhibit the sporadic contribution of occasional users who may not be willing to install additional software. To overcome this limitation, this work presents the Geo Collector Bot, an alternative software toolkit to empower field data collection projects avoiding the development and/or the installation of a specific mobile app on contributors’ devices. The Geo Collector Bot is a configurable Telegram-based chatbot enabling to dispatch of data collection forms that can be activated and filled in through Telegram chats. The ultimate goal of the presented work is to provide an alternative free and open-source software framework suitable for general-purpose field data collection applications. Development patterns and system architecture are described in detail alongside future improvements and outlooks for the Geo Collector Bot project

Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis

A SEMI-AUTOMATIC PROCEDURE FOR A DEMOGRAPHIC ANALYSIS OF THE FOSS4G DEVELOPERS' COMMUNITY

Abstract. The open and direct collaboration at the creation, improvement, and documentation of source code and software applications &amp;ndash; enabled by the web &amp;ndash; is recognized as a peculiarity of the Free and Open Source Software for Geospatial (FOSS4G) projects representing, at the same time, one of their main strengths. With this in mind, it turns out to be interesting to perform an extensive monitoring of both the evolution and the geographical arrangement of the developers' communities in order to investigate their actual extension, evolution and degree of activity. In this work, a semi-automatic procedure to perform this particular analysis is described. The procedure is mainly based on the use of the GitHub Search Application Programming Interface by means of JavaScript custom modules to perform a census of the users registered with a collaborator role to the repositories of the most popular FOSS4G projects, hosted on the GitHub platform. The collected data is processed and analysed using Python and QGIS. The results &amp;ndash; presented through tables, charts, and thematic maps &amp;ndash; allow describing both dimensions as well as the geographical heterogeneity of the contributing community of each individual project, while enabling to identify the most active countries &amp;ndash; in terms of the number of contributors &amp;ndash; in the development of the most popular FOSS4G. The limits of the analysis, including technical constraints and considerations on the significance of the developers' census, are finally highlighted and discussed.</p

Land use influence on ambient PM2.5 and ammonia concentrations: Correlation analyses in the Lombardy region, Italy

Air pollution is identified as the primary environmental risk to health worldwide. Although most of the anthropic emissions are due to combustion processes, intensive farming activities may also contribute significantly, especially as a source of particulate matter 2.5 and ammonia. Investigations on particulate matter and precursors dynamics, identifying the most relevant environmental factors influencing their emissions, are critical to improving local and regional air quality policies. This work presents an analysis of the correlation between particulate matter 2.5 and ammonia concentrations, obtained from the Copernicus Atmosphere Monitoring Service, and local land use characteristics, to investigate the influence of agricultural activities on the space-time pollutant concentration patterns. The selected study area is the Lombardy region, northern Italy. Correlation is evaluated through Spearman’s coefficient. Agricultural areas resulted in a significant factor for high ammonia concentrations, while particulate matter 2.5 was strongly correlated with built-up areas. Natural areas resulted instead a protective factor for both pollutants. Results provide data-driven evidence of the land use effect on air quality, also quantifying such effects in terms of correlation coefficients magnitude

Coherent Change Detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings

During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Amatrice and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service - European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts

Hotspot Analysis: an experimental Python plugin to enable LISA mapping into QGIS

The possibility of linking maps with statistical processes represents one of the meaningful advantages characterizing the latest generation of GIS software. In the last decades, manifold statistical techniques have been adapted as well as designed to enable geographic data analysis. Among these techniques, particularly popular - and widely adopted in many research fields - is the spatial autocorrelation analysis using LISA (Local Indicators for Spatial Association). LISA statistics are currently implemented into different programming libraries (e.g R- spdep https://cran.r-project.org/web/packages/spdep, Python-PySAL http://pysal.github.io, etc.), into Free and Open Source spatial statistical Software (eg. GeoDA http://geodacenter.github.io) as well as into proprietary GIS software suites. Within the most famous FOSS GIS, the access to LISA mapping capabilities is currently enabled only through command line while dedicated plugins have not been formally made available yet. We present here the Hotspot Analysis plugin, an experimental QGIS Python plugin aimed both to facilitate the access to to LISA mapping tools for users with no advanced programming skills – exploiting the user-friendly QGIS environment - as well as to contribute to the growth of the mapping capabilities of this FOSS GIS software. The Hotspot Analysis plugin is based mainly on the Exploratory Spatial Data Analysis (ESDA) module of PySAL and PyQGIS, providing a simplified interface to run LISA tools starting from vector layers. The stable version of plugin is available on the QGIS Python Plugins Repository (https://plugins.qgis.org/plugins/HotspotAnalysis ) while the development version as well as documentation and test data are available on GitHUB (https://github.com/danioxoli/HotSpotAnalysis_Plugin). The main plugin features, including installation requirements and computational procedures, are here described together with an example of the possible applications of the Hotspot analysis

WEB MAPPING ARCHITECTURES BASED ON OPEN SPECIFICATIONS AND FREE AND OPEN SOURCE SOFTWARE IN THE WATER DOMAIN

The availability of water-related data and information across different geographical and jurisdictional scales is of critical importance for the conservation and management of water resources in the 21st century. Today information assets are often found fragmented across multiple agencies that use incompatible data formats and procedures for data collection, storage, maintenance, analysis, and distribution. The growing adoption of Web mapping systems in the water domain is reducing the gap between data availability and its practical use and accessibility. Nevertheless, more attention must be given to the design and development of these systems to achieve high levels of interoperability and usability while fulfilling different end user informational needs. This paper first presents a brief overview of technologies used in the water domain, and then presents three examples of Web mapping architectures based on free and open source software (FOSS) and the use of open specifications (OS) that address different users' needs for data sharing, visualization, manipulation, scenario simulations, and map production. The purpose of the paper is to illustrate how the latest developments in OS for geospatial and water-related data collection, storage, and sharing, combined with the use of mature FOSS projects facilitate the creation of sophisticated interoperable Web-based information systems in the water domain

QGIS AND OPEN DATA CUBE APPLICATIONS FOR LOCAL CLIMATE ZONES ANALYSIS LEVERAGING PRISMA HYPERSPECTRAL SATELLITE DATA

Climate change poses a significant threat to humans and biodiversity, impacting various aspects of livelihoods, infrastructure, and ecosystems. Understanding climate change and its interaction with the environment is crucial for achieving Sustainable Development Goals. Local Climate Zones (LCZ) play a key role in comprehending climate change by categorizing urban areas also based on their thermal characteristics. This study presents prototype open-source software tools developed to integrate ground and satellite data for LCZ analysis in the Metropolitan City of Milan (Northern Italy). These tools consist of a QGIS plugin to access and preprocess ground-based meteorological sensor data and a client-server platform, based on the Open Data Cube and Docker technologies, for the exploitation of multispectral and hyperspectral satellite data in LCZ mapping and analysis. The tools’ architecture, data retrieval methods, and analysis capabilities are described in detail. The QGIS plugin facilitates the access and preprocessing of ground-based sensor data within the user-friendly QGIS environment. The platform enables seamless ground-sensor and satellite data management and analysis, using Jupyter Notebooks as an interface to support programmatic operations on the data. The proposed tools provide a framework for studying climate change and its local impacts on urban environments, with the potential of empowering users to effectively analyze and mitigate its effects

Extending accuracy assessment procedures of global coverage land cover maps through spatial association analysis

High-resolution land cover maps are in high demand for many environmental applications. Yet, the information they provide is uncertain unless the accuracy of these maps is known. Therefore, accuracy assessment should be an integral part of land cover map production as a way of ensuring reliable products. The traditional accuracy metrics like Overall Accuracy and Producer's and User's accuracies – based on the confusion matrix – are useful to understand global accuracy of the map, but they do not provide insight into the possible nature or source of the errors. The idea behind this work is to complement traditional accuracy metrics with the analysis of error spatial patterns. The aim is to discover errors underlying features which can be later employed to improve the traditional accuracy assessment. The designed procedure is applied to the accuracy assessment of the GlobeLand30 global land cover map for the Lombardy Region (Northern Italy) by means of comparison with the DUSAF regional land cover map. Traditional accuracy assessment quantified the classification accuracies of the map. Indeed, critical errors were pointed out and further analyses on their spatial patterns were performed by means of the Moran's I indicator. Additionally, visual exploration of the spatial patterns was performed. This allowed describing possible sources of errors. Both software and analysis strategies were described in detail to facilitate future improvement and replication of the procedure. The results of the exploratory experiments are critically discussed in relation to the benefits that they potentially introduce into the traditional accuracy assessment procedure