Open Spatial Data Research Articles

The Australian Park Life project: Development of a nationally standardised spatial layer and public participatory GIS for greenspace in Australian capital cities

This paper introduces the ‘Australian Park Life Project’ and describes a protocol to standardise the capture and collation of public open greenspace spatial data across Australian cities. This method will progress greenspace research allowing for unique coherent national analyses and comparative research across Australia. We also outline the development of the Park Life public participatory geographic information system (PPGIS) to spatially explore what, and how, public open greenspaces are being used by Australian communities. The combination of community crowdsourced spatial data providing location-specific information on the green public open spaces used, in combination with the national spatial layer of greenspace allows for unique analyses exploring the role of greenspace provision and design on use and represents a transformative strategy in shaping public open space policy and strategy.•A spatial layer of public open greenspace was created for the eight Australian State and Territory capital cities using a standardised data capture and collection method from local government planning schemes and land use data, and listings of managed and demarcated parks and reserves.•The Park Life public participatory geographic information system (PPGIS) was built to capture spatially referenced information on the use of greenspaces - specifically what spaces are used, and how they are used.

High-resolution harvester data for estimating rolling resistance and forest trafficability

AbstractInformation on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

Solar potential for social benefit: Maps to sustainably address energy poverty utilizing open spatial data in data poor settings

Access to affordable sustainable energy is a significant challenge for many low-income countries experiencing energy poverty. The United Nations (UN) Sustainable Development Goal (SDG) 7 aims to “ensure access to affordable, reliable, sustainable and modern energy for all.” Simultaneously, the European Union (EU) seeks to reduce emissions by 55% by 2030. Decarbonization policies must be carefully considered to avoid adverse effects on vulnerable groups experiencing energy poverty. Solar technology offers a viable solution to decarbonize the building sector, reduce energy dependence on fossil fuels, and provide financial benefits to the public. Mapping solar potential is crucial to determine where investment in solar photovoltaic (PV) technology is most advantageous to the populations who stand to benefit the most. We combine a solar potential mapping approach incorporating socio-economic indicators indicative of energy poverty, using off-the-shelf Geographic Information Systems (GIS) tools that are easily replicated across cities facing energy poverty. Utilizing lowest common denominator data and analysis approaches, we offer creative and innovative mapping solutions. The socio-economic factors help to contextualise the benefits of distributed PV systems and highlight the need for mapping solar potential, in combination with energy poverty indicators for sustainable planning and policymaking. Our results for a case study in the city of Plovdiv, Bulgaria, demonstrate high solar energy potential that could meet 29% of the city's electricity needs, save citizens about M€43.84 annually, and pinpoint where to invest first for the highest gains. Finally, we offer suggestions on how to use these results to inform decarbonization policies to benefit low-income populations that are often missed in existing energy policies.

Оценка обеспеченности территории Санкт-Петербурга наземным общественным транспортом с использованием геопространственного подхода

The authors propose a method for evaluating the public transport accessibility of a city, based on the spatial distribution of real estate and commercial property in its territory, it enables making a rating table of routes and stops. The relevance of the study is related to the high social importance of mass transit, since its accessibility creates a more comfortable urban environment and offers new opportunities to the residents. The significant growth of research on this topic is a result of the social need to improve the use of urban space. The rapid development in the field of big data processing makes it much more relevant to the real situation than any previous models. However, researchers often lack up-to-date information on the distribution of the population in a given territory, because the concept of state open spatial data in Russia is currently in its early stages, and city authorities may decide to make it confidential. The proposed quantitative approach to assessing the provision of urban areas with land transportation means is based on using spatial data available in open sources, as well as methods of geospatial analysis and modelling. It is used to study the public transport in St. Petersburg, RF; the technique has enabled identifying the main municipal routes as well as the parts of the city requiring network expansion

Does cycling infrastructure prioritize gentrifying neighborhoods? The case of Mexico City

ABSTRACTThis paper aims to test the existence of a relationship between gentrification and the presence of cycling infrastructure in central Mexico City using a sample of 555 neighborhoods in central Mexico City and drawing from open spatial data. This research is contextualized within a growing body of Latin American research on state-led gentrification and the role of infrastructure as a tool for urban revalorization. Results show the existence of a clear and significant correlation between cycling infrastructure and gentrification, revealing that the city’s improved bike infrastructure has overwhelmingly favored gentrifying neighborhoods, and support existing research on these programs as a tool of gentrification. In addition, when controlling for other variables, the level of cycling infrastructure acts as the main predictor of gentrification. These findings are compelling and have implications for mobility equity policy in the Mexico City context.

Health poverty among people with type 2 diabetes mellitus (T2DM) in Malaysia

In the context of the escalating burden of diabetes in low and middle-income countries (LMICs), there is a pressing concern about the widening disparities in care and outcomes across socioeconomic groups. This paper estimates health poverty measures among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Using data from the National Diabetes Registry between 2009 and 2018, the study linked 932,855 people with T2DM aged 40–75 to death records. Cox proportional hazards models were used to estimate the 5-year survival probabilities for each patient, stratified by age and sex, while controlling for comorbidities and area-based indicators of socio-economic status (SES), such as district-level asset-based indices and night-time luminosity. Measures of health poverty, based on the Foster-Greer-Thorbecke (FGT) measures, were employed to capture excessive risk of premature mortality. Two poverty line thresholds were used, namely a 5% and 10% reduction in survival probability compared to age and sex-adjusted survival probability of the general population. Counterfactual simulations estimated the extent to which comorbidities contribute to health poverty. 43.5% of the sample experienced health poverty using the 5% threshold, and 8.9% were health poor using the 10% threshold. Comorbidities contribute 2.9% for males and 5.4% for females, at the 5% threshold. At the 10% threshold, they contribute 7.4% for males and 3.4% for females. If all patients lived in areas of highest night-light intensity, poverty would fall by 5.8% for males and 4.6% for females at the 5% threshold, and 4.1% for males and 0.8% for females at the 10% threshold. In Malaysia, there is a high incidence of health poverty among people with diabetes, and it is strongly associated with comorbidities and area-based measures of SES. Expanding the application of health poverty measurement, through a combination of clinical registries and open spatial data, can facilitate simulations for health poverty alleviation.

Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas.

Open spatial data for the exploration of territories and digital services for accessing them

«Open Data» concept is presented considering its application for wide public access to spatial data. It is shown that some principles of the «Open Data» may not be applicable to spatial data due to their peculiarities. The information source «Navigator of Open Spatial Data» is being created at Moscow State University of Geodesy and Cartography to simplify access to geodata for training specialists in this field. Some aspects of the use of open spatial data are shown on the example of the Lunar data, since planetary data has the status of freely distributed. Digital services for access to planetary data are presented. Formats used for storage of spatial planetary data are discussed. Created interactive services and open lunar data accumulated at Moscow State University of Geodesy and Cartography provide free online access to the «Digital Moon» system, which can be considered as a collection of Lunar data, digital tools and infrastructure for processing of spatial data in the field of territory exploration. With growing artificial intelligence and machine learning technologies, the potential of the created «Digital Moon» system can be expanded, and the accumulated planetary spatial data will gain additional interdisciplinary connections.

The Green Space Availability in Ufa City Metropolis

For the administrative districts of the city of Ufa (the capital of the Republic of Bashkortostan, Russia), the distribution of green spaces, including public green spaces, the degree of greening, and the accessible green spaces per resident, were analysed. Green spaces were estimated to be available for the citizens within 400 m (for multi-story building areas), 800 m (for low-rise building areas), and 1250 m (for all residential areas). The analysis relies on open geographic system tools and open spatial data. Data are compared with existing governmental regulations and recommendations. Green stands to cover approximately 35% of the city area (indicating that Ufa is one of the greenest among the 16 million-plus Russian cities), and most forest sites are located outside the city centre. Meanwhile, public green spaces cover less than 5% of the city and are unevenly spread across administrative urban districts. The major recreation facilities are situated in the city centre. Despite the high level of greening in Ufa on the whole, access to public areas in multi-story building areas within a 400 m radius and low-rise building areas within an 800 m radius is not guaranteed for city residents.

A fourth way to the digital transformation: The data republic as a fair data ecosystem

Abstract To harness the promises of digital transformation, different players take different paths. Departing from corporate-driven (e.g., the United States) and state-led (e.g., China) approaches, in various documents, the European Union states its goal to establish a citizen-centric data ecosystem. However, it remains contentious the extent to which the envisioned digital single market can enable the creation of public value and empower citizens. As an alternative, in this article, we argue in favor of a fair data ecosystem, defined as an approach capable of representing and keep in balance the data interests of all actors, while maintain a collective outlook. We build such ecosystem around data commons—as a third path to market and state approaches to the managing of resources—coupled with open data (OD) frameworks and spatial data infrastructures (SDIs). Indeed, based on literature, we claim that these three regimes complement each other, with OD and SDIs supplying infrastructures and institutionalization to data commons’ limited replicability and scalability. This creates the preconditions for designing the main roles, rules, and mechanisms of a data republic, as a possible enactment of a fair data ecosystem. While outlining here its main traits, the testing of the data republic model is open for further research.

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities

Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward.

Mapping Agricultural Biodiversity: Legacy data and tensions between ways of seeing fields

Mapping is a core approach used to investigate and display spatial dynamics of biological diversity and habitats. In the Netherlands, agricultural lands occupy nearly two-thirds of the land surface and provide the greatest potential for habitat restoration; particularly in grassland-based dairy production systems, which comprise the largest share of these agricultural lands. When a crop rotation is applied to a long-term grassland, the resulting disruption of ecological complexity requires years–if not decades–to restore, even after reconversion. The availability of high-quality land-use data for measuring the spatio-temporal distribution of grassland legacies is thus essential for monitoring the dynamics of biodiversity in production grasslands. In this study, we reflect on the Basic Crop Registration (BRP) of the Netherlands, an open spatial data infrastructure developed for parcel-level crop registration and examine how it shapes our spatio-temporal understanding of land use. The BRP serves as an administrative basis for numerous national and local-level regulatory and financial arrangements, mainly aimed at agricultural actors. In this study, we repurposed BRP data to introduce a new perspective on depicting the stability of grasslands in a high-intensity agricultural region. We used this data to map the frequency of grassland-to-cropland conversions using 17 years of longitudinal crop records in southwest Friesland, Netherlands. The legacy effects of grassland-to-cropland conversion were investigated in a field study, where significant differences were found between new and long-term grasslands in plant community composition, soil organic matter content, bulk density, soil penetration resistance, and pH. In our analysis of BRP data, we discovered a significant number of grasslands that were recently converted from cropland but that were recorded as long-term grasslands. This affected approximately 12% of the study area from 2005–2021, which prevents the accurate tracking of grassland stability over time. This misclassification also adds uncertainty to the temporal context of the decline in grassland-dependent species in the region. However, using a spatially-explicit mapping approach, these misclassifications can be corrected and help produce an effective measure of grassland stability with potential as an agroecosystem monitoring tool for researchers, land-use planners, and policymakers.

Showcase of Active Learning and Teaching Practices in Spatial Data Infrastructure (SDI) Education

Abstract. The new concept of Open Spatial Data Infrastructures (Open SDIs) has emerged from an increased interest in open data initiatives together with national and international directives, such as the EU Open Data Directive (Directive (EU) 2019/1024), and the large investment of European public authorities in developing SDIs for sharing spatial data within public authorities. Open SDIs have the potential to boost reaching SDIs’ general aims and goals of facilitating the exchange and sharing of spatial data to support planning and decision-making by including public participation and increased openness in all aspects of SDIs, including Open SDI Education. The open SPatial data Infrastructure eDucation nEtwoRk (SPIDER) project aims to address Open SDI Education by particular emphasis on studying Active Learning and Teaching (ALT) methods for SDI education. This article provides a theoretical basis of ALT for SDI methodologies. We show in which way ALT practices were already implemented in SDI education at the Partner universities before the COVID-19 pandemic. We also describe how the pandemic functioned as a catalyst for implementing ALT practices to an online environment, and how students evaluated these practices. The outcomes of our research can serve as an inspiration for SDI education in other countries.

Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.

Cycling Planning in Small-Sized Brazilian Cities Based on Open Data Geoprocessing

The objective of this paper is to propose a method to systematize cycling planning in small-sized Brazilian cities based on open data, such as census data, collaborative mappings, and Digital Elevation Models, seeking to assist in the elaboration of municipal urban mobility plans, and that can be adapted to other countries with similar characteristics and that have equivalent information. The proposed method is summarized in four steps: geoprocessing of open spatial data, which allows the georeferencing of potential cycling demand and cycling trip generator poles, as well as updated representations of road systems; definition of the subsets of origins and destinations of the cycling routes to be identified using Dijkstra’s algorithm, in order to minimize the overall cost associated with the road segments' operational quality for cycling; impedance assignment to road segments based on their average slopes and cycling Levels of Traffic Stress; and assessment of these road segments’ proportions in the identified cycling routes (Cycling Potential Index). Examples of the proposed method’s application were carried out in two Brazilian cities, Bariri-SP and Bocaina-SP, through which it was possible to define, for both cities, continuous and interconnected cycling axles, accessible to most of the study areas’ potential cyclists. Therefore, despite the obvious importance of conducting specific research and field surveys in each Brazilian city for greater reliability of the results, the proposed method can contribute to the popularization of cycling in utilitarian trips and to the strengthening of the “bicycle culture” still in force in small-sized Brazilian cities.

Interoperability and Integration: An Updated Approach to Linked Data Publication at the Dutch Land Registry

Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster’s larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets.

Urban Poverty Mapping with Open Spatial Data: Evidence from Dar es Salaam

Urban Poverty Mapping with Open Spatial Data: Evidence from Dar es Salaam

Потенціал водної ерозії річки Конго в затоці Стенлі-пул

In this paper, a scientific study on the understanding of the erosion and transfer processes of suspended solids or sediments from the Congo River into the Stanley-Pool Bay was conducted. For this purpose, a treatment of rainfall data over three decades (1990-2020), parameters influencing the process of erosive dynamics leading to the realization of the water erosion map using the Universal Soil Loss Equation (USLE), was done. Using open access spatial data and GIS, this USLE model allowed quantifying the rate of material transported over the three decades: a spatialization of erosion risks on the right bank of the Congo River, notably in the Stanley-Pool Bay, a topography dominated by steep slopes up to 10%, a high erosivity and erodibility and a low soil protection were revealed. Finally, the results of the study show that about 40% of the study area is subject to soil loss. The erosion risk is very severe despite the vegetation cover.

Geofizičko postavljanje i seizmičnost u zoni Rift Malavi, Jugoistočna Afrika, analizirano kartografskim skriptiranjem

This paper describes a scripting cartographic techniques that automatically generate maps from open source spatial data using syntax of General Mapping Tools (GMT) and R. A case study present mapping East Africa with a focus on Malawi. In this study, two different approaches of scripting cartography using R programming language and GMT were studied for geophysical analysis aimed to visualize a series of eight new maps in Malawi: topography based on the GEBCO data, seismicity, geomorphometric modeling based on SRTM-90 m (slope, aspect, hillshade and elevation) and geophysical fields: geoid based on EGM-2008 and free-air Faye's gravity based on satellite derived gravity data from CryoSat-2 and Jason-1. In contrast to previous maps of Malawi, a scripting approach was introduced as a console-based cartographic mapping developed for plotting a series of thematic maps based on the high-resolution data. The maps demonstrate correlations between the topography and tectonic faults (Malawi Rift Zone) and earthquakes in the Malawi Lake and extent of landforms. The results demonstrate strong correspondence between the topography and geophysical fields (geoid and gravity): negative values of geoid (-15 to -20) are notable over the Malawi (Nyasa) Lake which corresponds with local topographic depressions. Free-air gravity fields reach the lowest values (-50 to -100) over the Malawi Lake. Local heights in gravity are compared with topographic mountain ranges in the NW and SW of the country on the borders with Zambia and Mozambique. The location of earthquakes vary with the majority located in the north. The geomorphological landforms demonstrate variability in slope steepness and aspect orientation shown on histogram. The techniques of scripts can be used to automatically map spatial data using raster datasets for geophysical visualization, and this paper demonstrated this through a variety of map from the presented thematic series of geophysical maps of Malawi. Full scripts used for mapping are available on the author's public GitHub repository with provided link to her open access codes.

Об опыте разработки ГИС-проекта кампуса университета

The article discusses the results of the work on the design and creation of a GIS project of the university campus. A model and methodology for creating a geographical information system (GIS) of the university campus was developed and tested in the Non-profit limited company “Manash Kozybayev North Kazakhstan university” setting. The GIS was created on the basis of open spatial data of the Remote Sensing of the Earth, materials of ground photography and aerial photography from an unmanned aerial vehicle (UAV). Geoinformation mapping and modeling was carried out using specialized programs: ArcGIS 10.4 (ESRI Inc.), Agisoft PhotoScan Professional Edition. The article presents the GIS structure of the campus, its main elements, and describes the stages of development. The work on the creation of GIS included the following stages: the development of its preliminary model and structure, the collection of the necessary initial information and data about the studied territory, the conduct of ground surveys and aerial photography, field work, processing of collected materials in special software applications, the creation of an electronic map and database, filling and correcting the latter. The campus GIS includes a digital cartographic framework and an associated database. Its structure contains a number of modules that combine thematic layers. Along with this, geo-referenced photorealistic three-dimensional models of university buildings were created during the research. A 3D model of the campus was built on the basis of aerial photography materials from the UAV. Developed GIS, its functionality can be useful to the university and find application in solving practical problems. The research was carried out within the framework of grant funding under the Sustainability Living Lab program.