H2GIS a spatial database to feed urban climate issues

International audienceTo understand the urban climate, predict the effect of urbanization or attend to improve the impact of some human activities, it is necessary to have a good understanding of the role of the urban surface. Indeed it has been demonstrated that surface forms affect urban microclimate (Givoni 1989, Oke 1981, 1988) and therefore changes the consumer behaviour of residents especially the building energy consumption (Santamouris, 2001, Ohashi et al., 2007).The urban territory is continuously changing: high-rise buildings densification, new road infrastructures, increase of impervious surfaces, consumption of agricultural and natural areas… The result is new border, new shapes and new morphology for the urban geometries. In this context, monitoring urban changes became a challenge for urban planners and decision makers.Geographical Information System (GIS) applications are increasingly being used to compute a set of indicators such as the Sky View Factor, the mean building height, the compactness ratio… All of theses indicators are used to study and monitor the urban structure (Long et al 2003, Bocher et al 2009). Besides, in the late 1990s, a large number of GIS-based tools have been developed by taking advantage of data organisation, spatial analysis and visualisation (eg. cartography). These three functions coincide with the focus of an indicator that needs to organize data, to quantify and to communicate.If this diversity is valuable, on the other hand it can also act as a disincentive for the scientists and urban stakeholders communities. These tools are often build to answer a particular subject (mono-thematic approach). Moreover, most of them are based on proprietary softwares which limits their distribution, the possibility to examine their implementation (algorithm) since the main software is required to run the tool (black box) (Steiniger and Bocher, 2009, Steiniger and Hay, 2009). Last but not least, the definitions used to compute an indicator may differ according to the authors.This situation is in sharp contrast with the needs of the scientific community to share results and experiences, and to experiment with new methods. Moreover, it is inconsistent with number of laws and regulations relating to the protection of the environment that promote common indicators.To fill this gap, we propose a new open source spatial database, called H2GIS (http://www.h2gis.org/), to manipulate and process geographic and alphanumeric data (Gouge et al, 2014). H2GIS is a spatial extension of the Relational Database Management System (RDBMS) H2 Database Engine (http://www.h2database.com/) in the spirit of PostGIS (http://postgis.net/). It adds support for managing spatial features and operations on thenew Geometry type of H2. H2GIS is fully compliant with the OGC’s Simple Features for SQL (SFSQL) 1.2.1 standards (Herring 2010, 2011).In this paper we show how the spatial RDBMS H2GIS should be an ideal framework to model the urban data (store and distinguish spatial relationships), create a generic set of spatial urban indicators and used them with massive data (scalable, multi-core processing).As an illustration, H2GIS is used in the MApUCE project which aims to integrate in urban policies and most relevant legal documents quantitative data from urban microclimate, climate and energy. Based on literature review, we offer an open spatial analysis toolbox to study the urban surface

Noise mapping based on participative measurements

The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higher level indicators, and to map the collected and processed data based on web services

Crowdsourcing of Noise Map Pollution using Smartphones: Journées des Laboratoires SIG de Suisse romande

We present at the LSSR journey, the ENERGIC-OD project and the application developed by the LAB-STICC (CNRS) and LEA (IFSTTAR) laboratories to collect noise data from smartphones.We present the ENERGIC-OD project and the application developed by the LAB-STICC (CNRS) and LEA (IFSTTAR) laboratories to collect noise data from smartphones

A geoprocessing framework to compute urban indicators: The MApUCE tools chain

International audienceA growing demand from urban planning services and various research thematics concerns urban fabric characterization. Several projects (such as WUDAPT) are currently lead in the urban climate field to answer this demand. However there is currently a need to propose standardized methods to calculate urban indicators and to automatically classify the urban fabric for any city in the world as well as to propose platforms to share these methods and the associated results. Our contribution answers partially to this challenge. A total of 64 standardized urban morphological indicators are calculated for three scales of analysis: building, block and a reference spatial unit (RSU). A supervised classification is performed for the building and the RSU scales using a regression trees model based on these indicators and on 10 urban fabric typological classes defined by urbanists and architects. A processing chain is proposed to realize indicator calculation and urban fabric classification for any french municipality according to reference data provided by the French National Geographical Institute (IGN). Spatial reasoning and morphological indicators description are formalized with SQL language and statistical analysis is carried out with R language. Finally a geoprocessing framework based on free and open source softwares, conform to the Open Geospatial Consortium (OGC) standards and ready to serve open data is built. Indicators values and classification results for 6% of the french municipalities (corresponding to 41% of all french buildings) are available through a web cartographic portal by any person interested in such analysis

Sky View Factor Calculation in Urban Context: Computational Performance and Accuracy Analysis of Two Open and Free GIS Tools

International audienceThe sky view factor (SVF) has an important role in the analysis of the urban micro-climate. A new vector-based SVF calculation tool was implemented in a free and open source Geographic Information System named OrbisGIS. Its accuracy and computational performance are compared to the ones of an existing raster based algorithm used in SAGA-GIS. The study is performed on 72 urban blocks selected within the Paris commune territory. This sample has been chosen to represent the heterogeneity of nine of the ten Local Climate Zone built types. The effect of the algorithms' input parameters (ray length, number of directions and grid resolution) is investigated. The combination minimizing the computation time and the SVF error is identified for SAGA-GIS and OrbisGIS algorithms. In both cases, the standard deviation of the block mean SVF estimate is about 0.03. A simple linear relationship having a high determination coefficient is also established between block mean SVF and the facade density fraction, confirming the results of previous research. This formula and the optimized combinations for the OrbisGIS and the SAGA-GIS algorithms are finally used to calculate the SVF of every urban block of the Paris commune

Openstreetmap convient-elle aux études sur le climat urbain ?

International audience; In order to assess the impact on cities on the regional climate, either present or future, an adequate description of the cities is needed. In global climate models, cities are most often not represented. In high-resolution climate models, they are now physically represented by dedicated surfaces schemes, as TEB (Town Energy Balance) model (Masson 2000). However, the urban description itself is most often very crude, based on land cover classes. First these land cover classes are at typically 1km of resolution, a coarse resolution from the stakeholders point of view, and secondly, the urban parameters associated with these classes are uniform for each class (same building height for all suburban areas for example). Recently, the Local Climate Zones concept has been proposed by Steward and Oke (2012), and is widely accepted as a reference in the urban climate community. It classifies the urban tissues and the surrounding land cover in classes (10 urban classes). Mills et al (2015) propose a methodology and a toolchain to build a world climate database on the physical geographies of cities. The World Urban Database and Portal Tool (WUDAPT) project categorizes data in 3 levels :- Level 0 describes a city in terms of its constituent neighbourhood types using the Local Climate Zone (LCZ) scheme (Stewart and Oke, 2012).- Level 1 refines the parameters for each LCZ through sampling. These data will capture information on UFF at a finer spatial resolution and in greater details.- Level 2 refines the data still further by integrating available data sources that can provide accurate parameter values at a fine spatial resolution, suited for boundary - layer modelling.The WUDAPT methodology is to use landsat images in order to classify the LCZ with pixels at 100m of resolution. Each city would be processed and validated by a scientist having local knowledge. While very promising to acquire urban data for urban climate modeling anywhere in the world, the resolution still is relatively coarse (100m), and there is no description of the morphological or architectural parameters yet. Those parameters are still uniform for each class. Bocher et al (2018) present an open geoprocessing framework to calculate standardized urban indicators at three geographic scales : building, block and a reference spatial unit (RSU). Called MApUCE database and based on a fine vector database provided by the French National Geographical Institute (IGN), it offers new opportunities to extend the WUDAPT database at a finest scale (with morphological, architectural and socioeconomic indicators). However because WUDAPT intends to classify the urban fabric by climate properties from homogeneous and available data at world scale, there is a need to investigate other databases.OpenStreetMap (OSM) is one of the most famous user-generated map. Its popularity is growing steadily, as evidenced by the number of users and the multiplication of uses. Due to its world geographic coverage , OSM constitutes an opportunity for environmental studies by opening-up possibilities for comparative scientific studies on several territories at the same time.This paper describes a methodology and a set of tools to check the availability of the OSM data to feed the MApUCE geoprocessing chain and thus urban climate studies. We propose an open source framework to :- Query on the fly the OSM database from a country code,- Compute spatial and attribute metrics on the country,- Store the results on a multi-dimensional database,- Visualise the results from a dashboard service that integrates chart and map representations at different scales : time, attributes, geography.; Afin d'évaluer l'impact des villes sur le climat régional, présent ou futur, une description adéquate de celles-ci est nécessaire. Dans les modèles climatiques globaux, le plus souvent, les villes ne sont pas représentées. Dans les modèles climatiques à haute résolution, elles sont maintenant représentées physiquement par des schémas de surfaces dédiés, comme le modèle TEB (Town Energy Balance) (Masson 2000). Cependant, la description urbaine elle-même est le plus souvent très grossière, basée sur les classes de couverture terrestre. D'une part, ces classes d'occupation du sol ont une résolution typique de 1 km, une résolution grossière du point de vue des parties prenantes, et d'autre part, les paramètres urbains associés à ces classes sont uniformes pour chaque classe (même hauteur de bâtiment pour toutes les zones suburbaines par exemple). Récemment, le concept de zones climatiques locales a été proposé par Steward et Oke (2012) et est largement accepté comme une référence dans la communauté climatique urbaine. Il classe les tissus urbains et la couverture végétale environnante en classes (10 classes urbaines). Mills et al (2015) proposent une méthodologie et une chaîne d'outils pour construire une base de données climatique mondiale sur la géographie physique des villes. Le projet WUDAPT (World Urban Database and Portal Tool) classe les données en trois niveaux :- Le niveau 0 décrit une ville en fonction des types de quartiers qui la composent à l'aide du schéma des zones climatiques locales ou Local Climate Zone(LCZ) (Stewart et Oke, 2012).- Le niveau 1 affine les paramètres de chaque LCZ par échantillonnage. Ces données permettront d'obtenir des informations sur l'UFF à une résolution spatiale plus fine et de manière plus détaillée.- Le niveau 2 affine encore davantage les données en intégrant les sources de données disponibles qui peuvent fournir des valeurs de paramètres précises à une résolution spatiale fine, adaptées à la modélisation des couches limites.La méthodologie WUDAPT consiste à utiliser des images Landsat afin de classer les LCZ en pixels à 100m de résolution. Chaque ville serait traitée et validée par un scientifique ayant des connaissances locales. Bien qu'il soit très prometteur d'acquérir des données urbaines pour la modélisation du climat urbain n'importe où dans le monde, la résolution est encore relativement grossière (100m), et il n'y a pas encore de description des paramètres morphologiques ou architecturaux. Ces paramètres sont toujours uniformes pour chaque classe. Bocher et al (2018) présentent un cadre de géotraitement ouvert pour calculer des indicateurs urbains normalisés à trois échelles géographiques : bâtiment, bloc et unité spatiale de référence ou reference spatial unit (RSU). Appelée base de données MApUCE et basée sur une base de données vectorielle fine fournie par l'Institut géographique national (IGN), elle offre de nouvelles possibilités d'étendre la base de données WUDAPT à une échelle plus fine (avec des indicateurs morphologiques, architecturaux et socio-économiques). Toutefois, étant donné que WUDAPT a l'intention de classer le tissu urbain par propriétés climatiques à partir de données homogènes et disponibles à l'échelle mondiale, il est nécessaire d'étudier d'autres bases de données.OpenStreetMap (OSM) est l'une des cartes les plus connues des utilisateurs. Sa popularité ne cesse de croître, comme en témoignent le nombre d'utilisateurs et la multiplication des usages. De par sa couverture géographique mondiale, OSM constitue une opportunité pour les études environnementales en ouvrant des possibilités d'études scientifiques comparatives sur plusieurs territoires en même temps.Cet article décrit une méthodologie et un ensemble d'outils permettant de vérifier la disponibilité des données d'OSM pour alimenter la chaîne de géotraitement MApUCE et donc les études climatiques urbaines. Nous proposons un framework open source pour :- Interroger à la volée la base de données d'OSM à partir d'un code pays,- Calculer les métriques spatiales et d'attributs sur le pays,- Stocker les résultats dans une base de données multidimensionnelle,- Visualiser les résultats à partir d'un service de tableau de bord qui intègre des représentations graphiques et cartographiques à différentes échelles : temps, attributs, géographie

OnoM@p : a Spatial Data Infrastructure dedicated to noise monitoring based on volunteers measurements

International audienceThis talk presents an ideal Spatial Data Infrastructure (SDI) dedicated to noise monitoring based on volunteers measurements. Called OnoM@P, it takes advantage of the geospatial standards and open source tools to build an integrated platform to manage the whole knowledge about a territory and to observe its dynamics. It intends also to diffuse good practices to organize, collect, represent and process geospatial data in field of acoustic researches. OnoM@p falls within the framework of the Environmental Noise Directive (END) 2002/49/CE. The system relies on the NoiseCapture Android application developed for allowing each citizen to estimate its own noise exposure with its smartphone

Openstreetmap convient-elle aux études sur le climat urbain ?

International audienceIn order to assess the impact on cities on the regional climate, either present or future, an adequate description of the cities is needed. In global climate models, cities are most often not represented. In high-resolution climate models, they are now physically represented by dedicated surfaces schemes, as TEB (Town Energy Balance) model (Masson 2000). However, the urban description itself is most often very crude, based on land cover classes. First these land cover classes are at typically 1km of resolution, a coarse resolution from the stakeholders point of view, and secondly, the urban parameters associated with these classes are uniform for each class (same building height for all suburban areas for example). Recently, the Local Climate Zones concept has been proposed by Steward and Oke (2012), and is widely accepted as a reference in the urban climate community. It classifies the urban tissues and the surrounding land cover in classes (10 urban classes). Mills et al (2015) propose a methodology and a toolchain to build a world climate database on the physical geographies of cities. The World Urban Database and Portal Tool (WUDAPT) project categorizes data in 3 levels :- Level 0 describes a city in terms of its constituent neighbourhood types using the Local Climate Zone (LCZ) scheme (Stewart and Oke, 2012).- Level 1 refines the parameters for each LCZ through sampling. These data will capture information on UFF at a finer spatial resolution and in greater details.- Level 2 refines the data still further by integrating available data sources that can provide accurate parameter values at a fine spatial resolution, suited for boundary - layer modelling.The WUDAPT methodology is to use landsat images in order to classify the LCZ with pixels at 100m of resolution. Each city would be processed and validated by a scientist having local knowledge. While very promising to acquire urban data for urban climate modeling anywhere in the world, the resolution still is relatively coarse (100m), and there is no description of the morphological or architectural parameters yet. Those parameters are still uniform for each class. Bocher et al (2018) present an open geoprocessing framework to calculate standardized urban indicators at three geographic scales : building, block and a reference spatial unit (RSU). Called MApUCE database and based on a fine vector database provided by the French National Geographical Institute (IGN), it offers new opportunities to extend the WUDAPT database at a finest scale (with morphological, architectural and socioeconomic indicators). However because WUDAPT intends to classify the urban fabric by climate properties from homogeneous and available data at world scale, there is a need to investigate other databases.OpenStreetMap (OSM) is one of the most famous user-generated map. Its popularity is growing steadily, as evidenced by the number of users and the multiplication of uses. Due to its world geographic coverage , OSM constitutes an opportunity for environmental studies by opening-up possibilities for comparative scientific studies on several territories at the same time.This paper describes a methodology and a set of tools to check the availability of the OSM data to feed the MApUCE geoprocessing chain and thus urban climate studies. We propose an open source framework to :- Query on the fly the OSM database from a country code,- Compute spatial and attribute metrics on the country,- Store the results on a multi-dimensional database,- Visualise the results from a dashboard service that integrates chart and map representations at different scales : time, attributes, geography.Afin d'évaluer l'impact des villes sur le climat régional, présent ou futur, une description adéquate de celles-ci est nécessaire. Dans les modèles climatiques globaux, le plus souvent, les villes ne sont pas représentées. Dans les modèles climatiques à haute résolution, elles sont maintenant représentées physiquement par des schémas de surfaces dédiés, comme le modèle TEB (Town Energy Balance) (Masson 2000). Cependant, la description urbaine elle-même est le plus souvent très grossière, basée sur les classes de couverture terrestre. D'une part, ces classes d'occupation du sol ont une résolution typique de 1 km, une résolution grossière du point de vue des parties prenantes, et d'autre part, les paramètres urbains associés à ces classes sont uniformes pour chaque classe (même hauteur de bâtiment pour toutes les zones suburbaines par exemple). Récemment, le concept de zones climatiques locales a été proposé par Steward et Oke (2012) et est largement accepté comme une référence dans la communauté climatique urbaine. Il classe les tissus urbains et la couverture végétale environnante en classes (10 classes urbaines). Mills et al (2015) proposent une méthodologie et une chaîne d'outils pour construire une base de données climatique mondiale sur la géographie physique des villes. Le projet WUDAPT (World Urban Database and Portal Tool) classe les données en trois niveaux :- Le niveau 0 décrit une ville en fonction des types de quartiers qui la composent à l'aide du schéma des zones climatiques locales ou Local Climate Zone(LCZ) (Stewart et Oke, 2012).- Le niveau 1 affine les paramètres de chaque LCZ par échantillonnage. Ces données permettront d'obtenir des informations sur l'UFF à une résolution spatiale plus fine et de manière plus détaillée.- Le niveau 2 affine encore davantage les données en intégrant les sources de données disponibles qui peuvent fournir des valeurs de paramètres précises à une résolution spatiale fine, adaptées à la modélisation des couches limites.La méthodologie WUDAPT consiste à utiliser des images Landsat afin de classer les LCZ en pixels à 100m de résolution. Chaque ville serait traitée et validée par un scientifique ayant des connaissances locales. Bien qu'il soit très prometteur d'acquérir des données urbaines pour la modélisation du climat urbain n'importe où dans le monde, la résolution est encore relativement grossière (100m), et il n'y a pas encore de description des paramètres morphologiques ou architecturaux. Ces paramètres sont toujours uniformes pour chaque classe. Bocher et al (2018) présentent un cadre de géotraitement ouvert pour calculer des indicateurs urbains normalisés à trois échelles géographiques : bâtiment, bloc et unité spatiale de référence ou reference spatial unit (RSU). Appelée base de données MApUCE et basée sur une base de données vectorielle fine fournie par l'Institut géographique national (IGN), elle offre de nouvelles possibilités d'étendre la base de données WUDAPT à une échelle plus fine (avec des indicateurs morphologiques, architecturaux et socio-économiques). Toutefois, étant donné que WUDAPT a l'intention de classer le tissu urbain par propriétés climatiques à partir de données homogènes et disponibles à l'échelle mondiale, il est nécessaire d'étudier d'autres bases de données.OpenStreetMap (OSM) est l'une des cartes les plus connues des utilisateurs. Sa popularité ne cesse de croître, comme en témoignent le nombre d'utilisateurs et la multiplication des usages. De par sa couverture géographique mondiale, OSM constitue une opportunité pour les études environnementales en ouvrant des possibilités d'études scientifiques comparatives sur plusieurs territoires en même temps.Cet article décrit une méthodologie et un ensemble d'outils permettant de vérifier la disponibilité des données d'OSM pour alimenter la chaîne de géotraitement MApUCE et donc les études climatiques urbaines. Nous proposons un framework open source pour :- Interroger à la volée la base de données d'OSM à partir d'un code pays,- Calculer les métriques spatiales et d'attributs sur le pays,- Stocker les résultats dans une base de données multidimensionnelle,- Visualiser les résultats à partir d'un service de tableau de bord qui intègre des représentations graphiques et cartographiques à différentes échelles : temps, attributs, géographie

Noise mapping based on participative measurements

The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higherlevel indicators, and to map the collected and processed data based on web services

GeoClimate: a Geospatial processing toolbox for environmental and climate studies

International audienceHuman activities induce changes on land use and land cover. These changes are most significant in urban areas where topographic features (e.g., building, road) affect the density of impervious surface areas and introduce a range of urban morphological patterns. Those characteristics impact the energy balance and modify the climate locally (e.g., inducing the so-called Urban Heat Island phenomenon).Therefore, there is a need for georeferenced morphological indicators as well as urban classifications (such as Local Climate Zones) that can be directly used for planning or as inputs of climate models. GeoClimate is dedicated to this purpose: it converts raw geographical data (OpenStreetMap and French BDTopo) into indicators useful for climate applications (sky view factor, vegetation fraction, etc.) However, its application is not limited to the climate field.The indicators calculated in GeoClimate can also be used for other diagnostic or planning purposes: studying the territory fragmentation, the influence of the urban fabric on pollution (noise or air chemical transport), the energy consumption, etc. GeoClimate is available as free and open source geospatial software