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The UN Open GIS Initiative is an ongoing Partnership Initiative leaded by the United Nations Geospatial Operations. The Initiative, established in March 2016, is supported by several UN Member States, UN Field Missions, UN Agencies and technology contributing partners (international organizations, academia, NGOs, and the private sector) and takes full advantage of their expertise.The target is the creation of an extended spatial data infrastructure that meets the requirements of the UN Secretariat (including UN field missions and regional commissions), and then expands to UN agencies, UN operating partners and developing countries. The paper presents the activities done in the past year and the status of the Initiative.

Indoor environments differ from outdoor in many aspects. This, added to the limitations faced by other common standards for urban features reinforced the need of setting a dedicated standard for indoor applications. IndoorGML was born in this context to provide the basic concepts, data models, and standard that meet the requirements of indoor spatial applications. Indoor spatial information can be generally classified into two categories: indoor objects such as architectural components (walls, stairs, slabs) and interior facilities (furniture); indoor spaces such as cavities (rooms and corridors) or virtual subdivision (sensor and legal spaces). Handling both information is necessary to support applications ranging from Indoor location-based services (LBS), indoor route analysis or indoor geo-tagging to building and asset management. In this paper, we present the proposed changes to the second version of IndoorGML, under preparation and intended to provide the necessary support for applications using information from those two categories. IndoorGML 2.0 is open to all applications that rely on indoor spaces and require analysis that can be performed on a network, extracted from those spaces utilizing neighbourhood relationships. It follows a model-driven approach, i.e. all concepts are presented by the Unified Modelling Language, from which technical implementations are derived (GML, JSON, SQL, etc.). We present the proposed changes to the previous version, illustrate a way of representing indoor objects other than spaces and discuss several use cases of the standard.

A main issue in developing e-government is about how to integrate and organize many complicated processes and different stakeholders. Interestingly geospatial information provides an efficient framework to integrate and organized them. In particular, it is very useful to integrate the process of land management in e-government with geospatial information framework, since most of land management tasks are related with geospatial properties. In this paper, we present a use-case on the e-government project in Kazakhstan for land management. We develop a geoportal to connect many tasks and different users via geospatial information framework. This geoportal is based on open source geospatial software including GeoServer, PostGIS, and OpenLayers. With this geoportal, we expect three achievements as follows. First we establish a transparent governmental process, which is one of main goal of e-government. Every stakeholder monitors what is happening in land management process. Second, we can significantly reduce the time and efforts in the government process. For example, a grant procedure for a building construction has taken more than one year with more than 50 steps. It is expected that this procedure would be reduced to 2 weeks by the geoportal framework. Third we provide a collaborative environment between different governmental structures via the geoportal, while many conflicts and mismatches have been a critical issue of governmental administration processes.

The status of the extended solar activity minimum, since the second half of 2007, has been briefly instructed to the solar-terrestrial community. Cycle 24 has the most spotless days since cycle 16, and probably even since the modern cycles, latitudes of high-latitude (>35°) sunspots belonging to a new cycle around the minimum time of the cycle are statistically the lowest at present, compared with those of cycle 12 onwards, and there is only one or no sunspots in a month appearing at high latitudes (>20°) for 58 months (from November 2003 to September 2008), which is observed for the first time since cycle 12 onwards. The solar wind velocity and pressure, 10.7 cm solar radio flux, the polar solar magnetic field, solar total irradiance, and so on reach their minima during the 23–24 cycle minimum time. In order to explain the present extreme low activity, we introduced here one possible mechanism using helio-seismology observations. Viewing, from the long-term running of the time scales of both the Gleissberg period and millenniums, the extended solar activity minimum becomes logical. According to the present observations, the cycle 24 should start in November 2008. Solar activity is predicted at being about 30% lower in cycle 24 than in cycle 23, synthesizing the typical predictions of solar activity, including those given by NASA and NOAA. The 24th solar cycle is sluggishly coming and it should be an opportune moment for studying solar physics and solar-terrestrial physics.

Urban carbon dioxide comprises the largest fraction of anthropogenic greenhouse gas emissions, but quantifying urban emissions at subnational scales is highly challenging, as numerous emission sources reside in close proximity within each topographically intricate urban dome. In attempting to better understand each individual source's contribution to the overall emission budget, there exists a large gap between activity-based emission inventories and observational constraints on integrated, regional emission estimates. Here we leverage urban CO2 observations from the BErkeley Atmospheric CO2 Observation Network (BEACO2N) to enhance, rather than average across or cancel out, our sensitivity to these hyperlocal emission sources. We utilize a method for isolating the local component of a CO2 signal that accentuates the observed intra-urban heterogeneity and thereby increases sensitivity to mobile emissions from specific highway segments. We demonstrate a multiple-linear-regression analysis technique that accounts for boundary layer and wind effects and allows for the detection of changes in traffic emissions on scale with anticipated changes in vehicle fuel economy – an unprecedented level of sensitivity for low-cost sensor technologies. The ability to represent trends of policy-relevant magnitudes with a low-cost sensor network has important implications for future applications of this approach, whether as a supplement to existing, sparse reference networks or as a substitute in areas where fewer resources are available.

The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding. Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2, and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO2N, a distributed network of approximately 50 sensor nodes, each measuring CO2, CO, NO, NO2, O3 and particulate matter at 10 s time resolution and approximately 2 km spacing within the San Francisco Bay Area.

A simple, practical model for estimating daily recharge - as hydrologically effective rainfall (HER) - to the Chalk outcrop of southern England is presented. Daily meteorological observations are the only data requirements. The model was calibrated for a Chalk river, the Wey, in south Dorset. Six different root constant thresholds were used to estimate daily actual evapotranspiration (AET) rates for the river. The model was then used to calculate HER using the six estimates of AET. Daily mean flow was simulated using three different models: CAPTAIN, IHACRES and INCA. The six HER estimates provided independent model inputs. HER calculated using a root constant of 200mm proved suitable not only for the Wey, but also (via a validation exercise) for other rivers on the Chalk of southern England for riverflow simulations as well as the timing and magnitude of groundwater recharge. The results suggest that a root constant of 200mm is optimal for the Chalk outcrop of southern England. The model is particularly useful for studies where the application of more complex methods of recharge estimation is impractical. Keywords: Chalk aquifer, root constant, recharge, Hydrologically Effective Rainfall, model, riverflow, CAPTAIN, IHACRES, INCA, River Wey

With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services.

The 3D triangle mesh is widely used to represent indoor space. One of widely used methods of generating 3D triangle mesh data of indoor space is the construction from the point cloud collected using LIDAR. However, there are many problems in using generated triangle mesh data as a geometric representation of the indoor space. First, the number of triangles forming the triangle mesh is very large, which results in a bottleneck of the performance for storage and management. Second, no consideration on the properties of indoor space has been done by the previous work on mesh simplification for indoor geometric representation. Third, there is no research to construct indoor spatial standard data from triangle mesh data. For resolving these problems, we propose the a method for generating triangular mesh data for indoor geometric representation based in the observations mentioned above. First this method removes unnecessary objects and reduces the number of surfaces from the original fine-grained triangular mesh data using the properties of indoor space. Second, it also produces indoor geometric data in IndoorGML – an OGC standard for indoor spatial data model. In experimental studies, we present a case study of indoor triangle mesh data from real world and compare results with raw data.

Recently, services and systems that deal with indoor spatial information are increasing. Each service or system adopts a data model that can store necessary indoor space data according to its purpose. However, since the content of indoor spatial information that can be expressed by each data model is differ and limited, it is necessary to exchange information between the systems in order to use rich indoor spatial data. OGC has published IndoorGML as the standard for exchange of indoor spatial information data between systems. To use IndoorGML as an exchange format, the software which supports IndoorGML construction is fundamental. But there are several limitations in the previous IndoorGML data editing tools. There is no editing tool that can generate all the features which are defined by IndoorGML. If users want to generate IndoorGML data, they need to consider the requirements of the IndoorGML. In this study, we implemented InFactory, which is a IndoorGML generation tool based on RESTful API supporting users to easily construct IndoorGML data. Users can easily create IndoorGML without knowledge on the schema and requirements of IndoorGML using InFactory. In addition, developers on IndoorGML data construction tools such as GUI editors do not have to implement duplicated IndoorGML generation program for their systems. Using Java API that supports CRUD on IndoorGML data, users can also deal with IndoorGML data in their applications.

Boosted by the dynamic urbanization of cities, indoor environments are getting more and more complex in order to be able to host people properly. While most of our time is spent inside buildings, the need of GIS tools to assist our daily activities that can become tedious, such as indoor navigation or facility management, became more and more urgent. In that perspective, the IndoorGML standard is aiming to address the gaps left by other standards regarding the spatial modelling for indoor navigation. It includes several concepts such as the organization of the spaces into cells along with their network representation and the possibility to represent multiple connected layers. However, being at its first stage, several concepts of the standard could be improved. One of these is the cell subspacing that is not enough discussed in the current version of the standard. In this paper, we explore all the aspects involved in the subdivision process, from the identification of the navigable and non-navigable space cells to the generation of a navigation graph. We propose several criteria on which the indoor sub-spacing can rely to be automatically performed and and illustrate them on a 3D indoor model.

Guidance and security in large public buildings such as airports, museums and shopping malls requires much more information that traditional 2D methods offer. Therefore 3D semantically-reach models have been actively investigated with the aim to gather knowledge about availability and accessibility of spaces. Spaces can be unavailable to specific users because of plenty of reasons: the 3D geometry of spaces (too low, too narrow), the properties of the objects to be guided to a specific part of the building (walking, driving, flying), the status of the indoor environment (e.g. crowded, limited light, under reconstruction), property regulations (private areas), security considerations and so on. However, such information is not explicitly avaible in the existing 3D semantically-reach models. IFC and CityGML are restricted to architectural building components and provide little to no means to describe such properties. IndoorGML has been designed to establish a generic approach for space identification allowing a space subdivision and automatic creation of a network for route computation. But currently it also represents only spaces as they are defined by the architectural layout of the building. The Land Administration Domain Model is currently the only available model to specify spaces on the basis of ownership and rights for use. In this paper we compare the principles of IndoorGML and LADM, investigate the approaches to define spaces and suggest options to the linking of the two types of spaces. We argue that LADM space subdivision on basis of properties and rights of use can be used to define to semantically and geometrically available and accessible spaces and therefore can enrich the IndoorGML concept.

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.