Spatial Data Quality Research Articles

The effect of Proteinase K treatment on GeoMx digital spatial profiling data quality from formalin-fixed, paraffin-embedded tissue

The emergence of spatial profiling technologies in recent years has accelerated opportunities to profile in detail the molecular attributes of a wide range of tissue pathologies using archival specimens. However, tissue treatment for fixation and storage does not always support generation of high-quality genomic data. The purpose of this study was to investigate the impacts of Proteinase K (ProtK) treatment, as a way to increase target transcript exposure, on downstream sequencing data quality metrics for spatial transcriptomic data using formalin-fixed, paraffin-embedded samples. In a series of four independent assessments using different tissue types (nasal mucosa, tonsil, pancreas), varying concentrations of ProtK (ranging from 0.1 to 1 μg/mL) were used as part of the sample processing workflow to generate transcriptomic data using the Nanostring GeoMx DSP and Illumina NextSeq 2000 platforms. Use of higher concentrations of ProtK was generally found to increase total reads (2-4-fold). However, negative probe counts also tended to be increased (2-12-fold), resulting in reductions in the signal-to-noise ratio (10-70% lower) and the number of genes detected above background (50-80% lower). These effects were not seen in all tissues and impacts of tissue handling and processing, beyond ProtK treatment, on data quality metrics, also require consideration. Regardless, these observations highlight the need for careful consideration of a range of sample processing factors and benefits that may be achieved through the optimisation of sample processing workflows for specific tissues as a way to maximise the generation of quality data using spatial transcriptomic approaches.

ANALISIS POLA PERUBAHAN LAHAN SAWAH MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (STUDI KASUS: KELURAHAN MAKROMAN, SAMARINDA)

Changes in rice fields are an important phenomenon that affects the sustainability of agriculture and the environment. This study aims to analyze the pattern of changes in rice fields in Makroman Village, using a Geographic Information System (GIS) approach. The method used is spatial analysis which involves such as spatial data creation, spatial data conversion, spatial data clustering, and mathematical calculation of spatial data. The spatial data used in this study include Ikonos Satellite Images of Makroman Village in 2011 and UAV Aerial Photos of Makroman Village in 2019. The results showed that the area of rice fields in Makroman Village increased from 96.196 ha to 129.343 ha in 2011-2019. Spatially, the increase occurred due to the presence of new rice fields and existing rice fields that did not decrease in number. Shrubs become land uses that have the potential to be converted in rice field extensification activities. GIS application is a method that can be used to answer the problems in this study to the level of detail, but some things that need to be considered in the application of this method are the quality of spatial data, especially remote sensing data used.

High-density generation of spatial transcriptomics with STAGE.

Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

A new R package to parse plant species occurrence records into unique collection events efficiently reduces data redundancy

Biodiversity data aggregators, such as Global Biodiversity Information Facility (GBIF) suffer from inflation of the number of occurrence records when data from different databases are merged but not fully reconciled. The ParseGBIF workflow is designed to parse duplicate GBIF species occurrence records into unique collection events (gatherings) and to optimise the quality of the spatial data associated with them. ParseGBIF provides tools to verify and standardize species scientific names according to the World Checklist of Vascular Plants taxonomic backbone, and to parse duplicate records into unique ‘collection events’, in the process compiling the most informative spatial data, where more than one duplicate is available, and providing crude estimates of taxonomic and spatial data quality. When GBIF occurrence records for a medium-sized vascular plant family, the Myrtaceae, were processed by ParseGBIF, the average number of records useful for spatial analysis increased by 180%. ParseGBIF could therefore be valuable in the evaluation of species’ occurrences at the national scale in support for national biodiversity plans, identification of plant areas important for biodiversity, sample bias estimation to inform future sampling efforts, and to forecast species range shifts in response to global climate change.

A geostatistical analysis of multiscale metallicity variations in galaxies – III. Spatial resolution and data quality limits

ABSTRACT Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these methods have only been applied to very high spatial resolution metallicity maps, leaving it uncertain if they will work on lower quality data. In this study, we apply geostatistical techniques to high-resolution spectroscopic maps of three local galaxies convolved to eight different spatial resolutions ranging from ∼40 pc to ∼1 kpc per pixel. We fit a geostatistical model to the data at all resolutions, and find that for metallicity maps where small-scale structure is visible by eye (with ≳10 resolution elements per Re), all parameters, including the metallicity correlation scale, can be recovered accurately. At all resolutions tested, we find that point metallicity predictions from such a geostatistical model outperform a circularly symmetric metallicity gradient model. We also explore dependence on the number of data points, and find that N ≳ 100 spatially resolved metallicity values are sufficient to train a geostatistical model that yields more accurate metallicity predictions than a radial gradient model. Finally, we investigate the potential detrimental effects of having spaxels smaller than an individual H ii region by repeating our analysis with metallicities integrated over H ii regions. We see that spaxel-based measurements have more noise, as expected, but the underlying spatial metallicity distribution can be recovered regardless of whether spaxels or integrated regions are used.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Optimizing Urban Traffic Management: Investigating the Impact of Probe Vehicle Road Coverage, Penetration Rates, and Speed Data Quality in Macroscopic Fundamental Diagram Estimation

Probe vehicles and loop detectors collect traffic data for empirical MFD estimation. The accuracy of MFD estimation is contingent upon the percentage of road network links covered (road coverage) and the extent of information (penetration rate) from each link. While the previous research has explored the effects of loop detector road coverage and probe vehicle penetration rates, the impact of probe vehicle road coverage remains unaddressed. Furthermore, the unequal distribution and low penetration rates of probe vehicles undermine the reliability of the data obtained from them. Therefore, this study aimed to (i) examine the impact of probe vehicle road coverage on MFD estimation, (ii) enhance the reliability of the spatial data by using speed data reconstruction, (iii) highlight the increased role of spatial data in the accuracy of MFD estimation, and (iv) develop a performance factor of speed data quality to demonstrate that the quality of spatial data is more susceptible to the penetration rate than road coverage. The results underlined the role of FCD road coverage in MFD estimation, and the proposed methodology significantly improves spatial data quality. The study concluded that spatial and temporal data affect the MFD estimation differently, and the proposed performance factor increases the role of spatial data in MFD estimation.

Investigating the distribution of a rare Colombo-Venezuelan kissing bug, Rhodnius neivai, Lent, 1953, using geographical information system-based analyses.

Rhodnius neivai, a kissing bug found in the dry regions of Colombia and Venezuela, has limited documented occurrences. While it is not deemed a significant vector for Chagas disease, distributional and ecological studies are essential in monitoring species domiciliation and shedding light on the evolutionary aspects of the Rhodniini tribe. The study aims to provide a detailed revision of R. neivai distribution and evaluate general spatial data quality for ecological niche modelling (ENM). It will also provide the first published ENM for the species, which may aid species sampling and future analytical improvement. Registers and other spatial information were gathered by literature review; data georeferencing, preliminary geographical investigations, and model editing were conducted in GIS platforms; ENMs were built using R and explored the uncertainty of parameters and algorithms. Twenty four unique sites were identified, unearthing 17 previously uncovered records. Data lacks robust spatial and temporal precision; however, ENMs had acceptable validations. The models present some variation in suitability but with objective areas for sampling effort. Rhodnius neivai distribution is better explained by conditions that characterise dry ecotypes, but further sampling is essential to improve modelling and advance with ecological and evolutive matters.

Joint simplification of various types spatial objects while preserving topological relationships

Cartographic generalization includes the process of graphically reducing information from reality or larger scaled maps to display only the information that is necessary at a specific scale. After generalization, maps can show the main things and essential characteristics. The scale, use and theme of maps, geographical features of cartographic regions and graphic dimensions of symbols are the main factors affecting cartographic generalization. Geometric simplification is one of the core components of cartographic generalization. The topological relations of spatial features also play an important role in spatial data organization, queries, updates, and quality control. Various map transformations can change the relationships between features, especially since it is common practice to simplify each type of spatial feature independently (first administrative boundaries, then road network, settlements, hydrographic network, etc.). In order to detect the spatial conflicts a refined description of topological relationships is needed. Considering coverings and mesh structures allows us to reduce the more general problem of topological conflict correction to the problem of resolving topological conflicts within a single mesh cell. In this paper, a new simplification algorithm is proposed. Its peculiarity is the joint simplification of a set of spatial objects of different types while preserving their topological relations. The proposed algorithm has a single parameter — the minimum map detail size (usually it is equal to one millimeter in the target map scale). The first step of the algorithm is the construction of a special mesh data structure. On its basis for each spatial object a sequence of cells is formed, to which points of this object belong. If a cell contains points of only one object, its geometric simplification is performed within the bounding cell using the sleeve-fitting algorithm. If a cell contains points of several objects, geometric simplification is performed using a special topology-preserving procedure.

Critical Issues in the Methods of Data Collection in Geoinformatics and Environmental Sciences

A successful geographic or environmental research is partly dependent on the sources and methods of data collection, data analysis and result presentation. This study examined two broad sources of data collection to include (i) primary: direct field or in-situ measurements, count, observation, administration of questionnaire, use of check list and Focus Group Discussions, and (ii) secondary: documented materials (including published and unpublished statistics/materials) residents in government and Non-Governmental Agencies (such as World Health and Food and Agricultural organizations), and private geoinformation organizations. Other sources of secondary data collection identified include research institutes such as the National Space Research and Development Agency (NASRDA) for the provision of Satellite Images for landuse and environmental studies, and Universities that deals with science and technology issues such as the Departments of Geography, Environmental Sciences, Urban and Regional Planning and Surveying/Geoinformatics. The evaluated major materials/tools/equipment used for data collections. The findings revealed that although primary data seems more reliable because of the close watch and methods of their collection, the cost and time it takes to generate them are very expensive hence, most people tend to rely on secondary sources of data for their use. The secondary sources may be relatively cheap but may be fraught with Spatial Data Quality issues. The study demonstrated that both primary and secondary datasets proved very useful in geographic and environmental change studies. A review of an empirical study of ‘urbanization impact on climate change in West Africa city of Warri, Delta State, Nigeria’ was used to validate this study.

Sustainable Development Goals from Theory to Practice Using Spatial Data Infrastructure: A Case Study of UAEU Undergraduate Students

The importance of Education for Sustainable Development (ESD) in influencing students’ understanding of and behavior toward sustainability cannot be overstated. However, prior studies have shown that students generally lack comprehension of how to apply geography instruction in relation to territories and their contribution to the Sustainable Development Goals (SDGs). Although the relationship between the SDGs and academic institutions has been the subject of numerous research, it is still unknown how much knowledge students have on the subject. Therefore, this research aims to raise awareness about SDGs and evaluate the knowledge of undergraduate geography students regarding the SDGs. Additionally, it investigates the impact of Spatial Data Infrastructure (SDI) and quality education as pedagogical tools on students’ sustainability consciousness. A questionnaire was designed, validated, and administered to students at the United Arab Emirates University from both geography and non-geography cohorts to assess their knowledge. Statistical analysis indicated high reliability of the constructs. The results revealed significant insights through descriptive, ANOVA, and multiple comparisons analysis with the Tukey HSD test. Specifically, geography students who participated in an SDG Awareness and Knowledge Program within the Technological Pedagogical Content Knowledge (TPACK) model demonstrated statistically significant differences in various aspects of SDG awareness and knowledge, SDG awareness using SDI skills, and the importance of quality education and the integration of SDG knowledge in pedagogy, as compared to other geography and non-geography students who did not receive the SDG awareness program. This research is expected to provide valuable knowledge about SDGs through effective pedagogical skills, benefiting both student and educator community.

A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation

ABSTRACT Similarity measurement has been a prevailing research topic in geographic information science. Geometric similarity measurement in scaling transformation (GSM_ST) is critical to ensure spatial data quality while balancing detailed information with distinctive features. However, GSM_ST is an uncertain problem due to subjective spatial cognition, global and local concerns, and geometric complexity. Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights, leading to poor robustness in addressing GSM_ST. This study proposes an unsupervised representation learning framework for automated GSM_ST, using a Graph Autoencoder Network (GAE) and drainage networks as an example. The framework involves constructing a drainage graph, designing the GAE architecture for GSM_ST, and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales. We perform extensive experiments and compare methods across 71 drainage networks during five scaling transformations. The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88% and has strong robustness. Moreover, our proposed method also can be applied to other scenarios, such as measuring similarity between geographical entities at different times and data from different datasets.

Spatial data trusts: an emerging governance framework for sharing spatial data

ABSTRACT Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes, backed by the confidence of clear and unambiguous data governance. Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust. The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications, addressing some longstanding barriers to data sharing, such as location privacy and data sovereignty. This paper introduces and explores the concept of a ‘spatial data Trust’ by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data. The work identifies five key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in the field, including spatial data infrastructures (SDIs), location privacy, and spatial data quality. The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

Methodology of assessing quality of spatial data describing course of shoreline as tool supporting water resource management process

Methodology of assessing quality of spatial data describing course of shoreline as tool supporting water resource management process

Analysis of the impact of spatial data quality in the decision-making process: choice of wind turbine site in Burundi

Analysis of the impact of spatial data quality in the decision-making process: choice of wind turbine site in Burundi

A comprehensive framework for evaluating the quality of street view imagery

Street view imagery (SVI) is increasingly in competition with traditional remote sensing sources and assuming its domination in myriads of studies, mainly thanks to the omnipresence of commercial services such as Google Street View. Similar to other spatial data, SVI may be of variable quality and burdened with a variety of errors. Recently, this concern has been amplified with the rise of volunteered SVI such as Mapillary and KartaView, which – akin to other instances of Volunteered Geographic Information (VGI) – are of heterogeneous quality. However, unlike with many other forms of spatial data, there has not been much discussion about the quality of SVI datasets, let alone a standard and mechanism to assess them. Further, current spatial data quality standards are not entirely applicable to SVI due to its particularities. Following a multi-pronged method, we establish a comprehensive framework for describing and assessing the quality of SVI. We present a categorised set of 48 elements that suggest the quality of imagery and associated data such as geographic information and metadata. The framework is applicable to any source of SVI, including both commercial and crowdsourcing services. In the implementation, which we release open-source, we assess several quality elements of SVI datasets across 9 cities. The results expose varying quality of SVI and affirm the importance of the work. Given the exponential volume of studies taking advantage of SVI, but largely overlooking quality aspects, this work is a timely contribution that will benefit data providers, contributors, and users. It may also be applied on other forms of image-based VGI, and underpin establishing a formal international standard in the future. On a broader perspective, while providing an overdue definition of SVI, this work also reveals issues and open questions that impede delineating and assessing this diverse form of urban and terrestrial imagery. • We propose the first comprehensive quality framework for street view imagery. • Framework comprises 48 quality elements and may be applied to other image datasets. • We implement partial evaluation for data in 9 cities, exposing varying quality. • The implementation is released open-source and can be applied to other locations. • We provide an overdue definition of street view imagery.

InstantCITY: Synthesising morphologically accurate geospatial data for urban form analysis, transfer, and quality control

Generative Adversarial Network (GAN) is widely used in many generative problems, including in spatial information sciences and urban systems. The data generated by GANs can achieve high quality to augment downstream training or to complete missing entries in a dataset. GANs can also be used to learn the relationship between two datasets and translate one into another, e.g. road network data into building footprint data. However, such approach has not been developed in the geospatial and urban data science context, its usability remains unknown, and the methods are not fully developed. We develop a new Geographical Data Translation algorithm based on GAN to generate high-resolution vector building data solely from street networks, which may be used to predict the urban morphology in absence of building data, also enabling studies in unmapped or undermapped urban geographies, among other advantages. Experiments on 16 cities around the world demonstrate that the generated datasets are largely successful in resembling ground truth morphologies. Thus, the approach may be used in lieu of traditional data for tasks that are often hampered by lack of data, e.g. urban form studies, simulation of urban morphologies in new contexts, and spatial data quality assessment. Our work proposes a novel rapid approach to generate building footprints in replacement of procedural methods and it introduces a new intrinsic method for large-scale spatial data quality control, which we test on OpenStreetMap by predicting missing buildings and suggesting the completeness of data without the usually required authoritative counterparts. The code, sample model, and dataset are available openly.

Mining real estate ads and property transactions for building and amenity data acquisition

Acquiring spatial data of fine and dynamic urban features such as buildings remains challenging. This paper brings attention to real estate advertisements and property sales data as valuable and dynamic sources of geoinformation in the built environment, but unutilised in spatial data infrastructures. Given the wealth of information they hold and their user-generated nature, we put forward the idea of real estate data as an instance of implicit volunteered geographic information and bring attention to their spatial aspect, potentially alleviating the challenge of acquiring spatial data of fine and dynamic urban features. We develop a mechanism of facilitating continuous acquisition, maintenance, and quality assurance of building data and associated amenities from real estate data. The results of the experiments conducted in Singapore reveal that one month of property listings provides information on 7% of the national building stock and about half of the residential subset, e.g. age, type, and storeys, which are often not available in sources such as OpenStreetMap, potentially supporting applications such as 3D city modelling and energy simulations. The method may serve as a novel means to spatial data quality control as it detects missing amenities and maps future buildings, which are advertised and transacted before they are built, but it exhibits mixed results in identifying unmapped buildings as ads may contain errors that impede the idea.

Cadastre Typology as a Baseline for Incremental Improvement of Spatial Cadastre in Jakarta: Towards a Complete Cadastre

Improving the quality of spatial cadastre remains a challenge in Indonesia. The lack of data quality impacts the legal uncertainty of land rights and the inequality of control, ownership, use, and utilization of land. This study discusses the efforts that can be made to achieve an accurate, assured, and authoritative spatial cadastre by referring to cadastral regulations in Indonesia, especially in urban areas. This research focuses on three spatial elements: boundary determination survey, measurement methods, and base map accuracy in order to assess systematic and sporadic registration activities previously conducted in two sub-districts in North Jakarta. The areas are located in a compacted urban area that consists of 19,173 land parcels as a research sample. A multivariate clustering tool is used to analyze the grouping of land parcels into a cadastral typology (comply/not comply). This study indicates that the level of compliance of the land parcel maps against three spatial elements are the following: (1) the compliance to boundary determination survey by 100%; (2) the compliance to measurement method by 17.36%; and (3) the compliance to base map accuracy by 0%. This paper explains how the cadastre typology can be used as an indicator of compliance as well as a baseline to improve the quality of spatial data.