Data Repository of the International Institute of Applied Systems Analysis
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Exploring and promoting green urban spaces in Vienna - can data about public perception help drive change?
No full textCitizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues.
The Horizon 2020 project, LandSense, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by engaging citizens to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through mobile and web applications, allowing citizens to play a key role in environmental monitoring. Within this project, the City Oases mobile application, focused on the city of Vienna, has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. City Oases was launched in March 2019.
Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer’s day? Open urban spaces can be used in many ways. Pick an activity in the CityOases app and we show the spots where you can do them, including the rating of previous users and pictures from the location. If you visit the spot you can rate it as well based on a few selected subjective criteria. If you know a cool spot that is openly accessible but not marked in our map yet? Just add it with a list of activities and some pictures. Additionally, you can input your perceptions including whether it is noisy, clean or if the infrastructure is attractive. The picture module within the application promotes you to take photos in the four cardinal directions. Users can search for specific activities, visit one of the points indicated on the map and then evaluate this point
Data for: A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand
No full textThe dataset consists of nine tiles of 16-bit GeoTIFF at a resolution of 30 m with a single attribute value, i.e., the year in which the
oil palm plantation was first detected. At this point the plantation is 2 to 3 year of age.
The data values range from 0 to 37 where 0 is no data and subsequent numbers run through until 2017, the last year of our analysis. A value of 4 corresponds to the year 1984, the first year oil palm was detected and 37 corresponds to 2017. Values 1 to 3 are not present
Supplementary data for: Global Gridded Nitrogen Indicators: Influence of Crop Maps
No full textNitrogen (N) indicators on a global grid pose unique opportunities to quantify environmental impacts from N application simultaneously on various scales and different world regions. Such calculations require the use of maps showing the geo-spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still needs to be evaluated. In this study we analyze the differences of the results for Nitrogen Use Efficiency (NUE) and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origin of the arising differences between the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania and North Africa. However, we also found that the in- or exclusion of grass crops influences the results as does the aggregation of crops to categories. Considering all these differences we note that for the calculation of N indicators M3 seems to provide the more plausible results. Our analysis not only highlights the importance of isolating the critical inputs, but also allows to consistently identify important parameters connected with N use and overuse on the global scale.
This dataset contains:
• All gridded N input and output data (provided in netCDF format and includes a description of the calculation)
• Gridded results of N surplus based on SPAM and M3 (provided in netCDF format and includes a description of the calculation)
• NUE per country (comparison of data by Leip et al., 2009, Lassaletta et al., 2014 and variations of our SPAM and M3 based calculations)
• N content per crop and their sources for all crops in M3 and SPAM
• Maps of N surplus for the SPAM based calculation and the M3 based calculatio
Soil respiration database
No full textSoil respiration (Rs) in situ measurements that were reported in peer-reviewed publications were collected into a database. The database contains annual Rs flux, share of autotrophic Rs, climate parameters, type of soil and vegetation. 3881 records on Rs fluxes around the globe were collected from 944 studies, spanning the measurement years 1961-2019. The largest portion of data was taken from the global database by Bond-Lamberty and Thomson (2014, 2018). We have taken from this database only the records where annual Rs flux or mean seasonal rate of Rs or root contribution to the Rs were reported. Data from additional 302 sources were collected on the same basis, from the northern hemisphere, with a special focus on Russia. The regions most frequently represented are Northern America (n=1835), Europe (n=1171) and Asia (n=872). Data from temperate ecosystems dominate in the database (n=1833), boreal zone is represented by 958 records, subtropical and tropical biomes are represented by 462 and 628 records accordingly. The most data collected in forests (n=2510), grasslands (n=520) or arable land (n=519)
Supplementary data for: Global Gridded Nitrogen Indicators: Influence of Crop Maps
No full textDisplaying Nitrogen (N) indicators on a global grid poses unique opportunities to quantify environmental impacts from N application in different world regions under a variety of conditions. Such calculations require the use of maps showing the geo-spatial distribution of crop production. Although there are several crop maps in the scientific literature to choose from, the consequences of this choice for the calculation of N indicators still need to be evaluated. In this study we analyze the differences in results for NUE and N surplus calculated on the global scale using two different crop maps (SPAM and M3). For our calculations we used publicly available statistical and literature data combined with each crop map and carefully traced the origins of the differences in the results. Our results showed that the regions most affected by discrepancies caused by differences in crop maps (yields and physical area) are Central Asia and the Russian Federation, Australia and Oceania, and North Africa. However, we also found that the inclusion or exclusion of grass crops influences the results, as does the aggregation of crops to categories. Considering all these differences, we note that M3 seems to provide the more plausible results for the calculation of N indicators. Our analysis not only highlights the importance of determining the critical parameters for N indicator calculation, but also allows key parameters connected with N use and overuse to be identified on the global scale.
This dataset contains:
All gridded N input and output data (provided in netCDF format and includes a description of the calculation)
Gridded results of N surplus based on SPAM and M3 (provided in netCDF format and includes a description of the calculation)
NUE per country (comparison of data by Leip et al., 2009, Lassaletta et al., 2014 and variations of our SPAM and M3 based calculations)
N content per crop and their sources for all crops in M3 and SPAM
Maps of N surplus for the SPAM based calculation and the M3 based calculation
Supplementary Informatio
Data related to research project: Water Futures and Solution - Fast Track Initiative
No full textThe Water Futures and Solutions Initiative (WFaS) is a cross-sector, collaborative global water project. Its objective is to apply systems analysis, develop scientific evidence and identify water-related policies and management practices, working together consistently across scales and sectors to improve human well-being through water security. The approach is a stakeholder-informed, scenario-based assessment of water resources and water demand that employs ensembles of state-of-the-art socio-economic and hydrological models, examines possible futures and tests the feasibility, sustainability and robustness of options that can be implemented today and can be sustainable and robust across a range of possible futures and associated uncertainties. This report aims at assessing the global current and future water situation
Implied emission factors in the World Bank’s Carbon Pricing Assessment Tool (CPAT)
No full textThe implied emission factors in CPAT are based on the GAINS model. The GAINS model methodology is described in (Amann et al., 2011). It is an integrated assessment model of air pollution, and features both an emissions model for various air pollutants and greenhouse gases (relevant here are primary PM2.5, SO2, NOx, NH3 and VOC, as well as CO2, CH4, BC, OC), as well as impact modules (which have not been used in the present exercise)
Historical CO2 emissions from land-use and land-cover change and their uncertainty
No full textMain results of our 2020 study on historical land-use and land-cover change emissions. These emissions were obtained using the compact Earth system model OSCAR v3.1. Two files are provided: one with only the best-guess estimates of the main land-to-atmosphere carbon fluxes, broken down along several axes (regions, biomes, and carbon pools); and a larger one with the results of all experiments and all 10,000 Monte Carlo elements, as well as the weights used to estimate best guesses and uncertainties, albeit detailed only on the regional axis. Additional information can be found in the corresponding paper
Economic Forecasting with an Agent-Based Model
No full textThis collection contains observed time series data, simulation results, model codes and executables used to produce the simulation results of Poledna S, Miess MG, & Hommes C
Crowd and community sourcing to update authoritative LULC data in urban areas
No full textThe French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the Paysages mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.
Dataset 1: Change detection validation 2019
This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: Paysages web application and LACO-Wiki. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.
The dataset has the following characteristics:
Time period of the change detection: 2016-2019.
Time period of data collection: February 2019-December 2019
Total number of contributors: 105
Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.
Region of interest: Toulouse and surrounding areas
Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON
Dataset 2: Land use classification 2019
The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (OCS-GE 2016 ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location.
The dataset has the following characteristics:
Time period of data collection: August 2019
Types of contributors: Surveyors from the production department of IGN
Total number of contributors: 5
Total number of observations: 2711
Data specifications of the OCS-GE ©IGN
Region of interest: Toulouse and surrounding areas
Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON
Dataset 3: In-situ validation 2018
The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined:
Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).
Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.
The dataset has the following characteristics
Time period of data collection: June 2018 – October 2018
Types of contributors: students from the School of Agricultural and Life Sciences and citizens
Total number of contributors: 26
Total number of observations: 281
Total number of photos: 421
Region of interest: Toulouse and surrounding areas
Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSO