On Quantifying Qualitative Geospatial Data: A Probabilistic Approach

Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based maps visualizing the spatial extent of the various relations

The LSST Data Mining Research Agenda

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

AKARI Far-Infrared All Sky Survey

We demonstrate the capability of AKARI for mapping diffuse far-infrared emission and achieved reliability of all-sky diffuse map. We have conducted an all-sky survey for more than 94 % of the whole sky during cold phase of AKARI observation in 2006 Feb. -- 2007 Aug. The survey in far-infrared waveband covers 50 um -- 180 um with four bands centered at 65 um, 90 um, 140 um, and 160 um and spatial resolution of 3000 -- 4000 (FWHM).This survey has allowed us to make a revolutionary improvement compared to the IRAS survey that was conducted in 1983 in both spatial resolution and sensitivity after more than a quarter of a century. Additionally, it will provide us the first all-sky survey data with high-spatial resolution beyond 100 um. Considering its extreme importance of the AKARI far-infrared diffuse emission map, we are now investigating carefully the quality of the data for possible release of the archival data. Critical subjects in making image of diffuse emission from detected signal are the transient response and long-term stability of the far-infrared detectors. Quantitative evaluation of these characteristics is the key to achieve sensitivity comparable to or better than that for point sources (< 20 -- 95 [MJy/sr]). We describe current activities and progress that are focused on making high quality all-sky survey images of the diffuse far-infrared emission.Comment: To appear in Proc. Workshop "The Space Infrared Telescope for Cosmology & Astrophysics: Revealing the Origins of Planets and Galaxies". Eds. A.M. Heras, B. Swinyard, K. Isaak, and J.R. Goicoeche

Geospatial data quality indicators

Indicators which summarise the characteristics of spatiotemporal data coverages significantly simplify quality evaluation, decision making and justification processes by providing a number of quality cues that are easy to manage and avoiding information overflow. Criteria which are commonly prioritised in evaluating spatial data quality and assessing a dataset’s fitness for use include lineage, completeness, logical consistency, positional accuracy, temporal and attribute accuracy. However, user requirements may go far beyond these broadlyaccepted spatial quality metrics, to incorporate specific and complex factors which are less easily measured. This paper discusses the results of a study of high level user requirements in geospatial data selection and data quality evaluation. It reports on the geospatial data quality indicators which were identified as user priorities, and which can potentially be standardised to enable intercomparison of datasets against user requirements. We briefly describe the implications for tools and standards to support the communication and intercomparison of data quality, and the ways in which these can contribute to the generation of a GEO label

Performance of Quality Assurance Procedures for an Applied Climate Information System

Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of 1C for temperature, not too different from the sensor field accuracy. The study underscores the fact that precipitation is more difficult to quality control than temperature. The new spatial regression test presented in this document outperformed all the other tests, which together identified only a few errors beyond those identified by the spatial regression test

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

Analyzing Spatial and Non-Spatial Factors that Influence Educational Quality in Primary Schools of Emerging Regions: Evidence from Geospatial Analysis and Administrative Time Series Data (The Case of Gambela City, Gambella Regional State, Ethiopia, East A

It is universally agreed concept that education is a corner stone for socio economic transformation.  Education has been recognized as weapon to fight backwardness, poverty and illiteracy for ages. Experience have shown that all the good benefit of education has been assured only when there is quality education. However, there are visible indicators that education quality has not been still achieved in the entire world. Particularly, education quality in developing countries is perceived as discouraging. Hence, the sharp decline of education quality is becoming major concern for developing countries like Ethiopia. This is also more serious concern in emerging regions like Gambella regional state where most people share pastoralism way of life. It is also understandable fact that there are locally known spatial and non-spatial factors that affect education quality. Therefore, the main objective of this study was to identify spatial and non-spatial factors that influence educational quality of primary schools in emerging regions. As methodological approach, the study was used descriptive design approach with mix of qualitative and quantitative method. The main data sources were both primary and secondary data. Primary data were collected from spatial and non-spatial data sources. The spatial information was mainly collected from GPS reading, aerial photo and land use plan. Non spatial primary data were collected using interviews and discussions. The perceptions and reflections of key informants (teachers, students, principals and parents) were entertained by using focus group discussion, key informant interview and public hearings. The secondary data sources were collected by means of desk review. As result, the study was found that education quality in the city is weakening. The underlying causes are, low teachers motivation level, high students- teacher ratio, high students- section ratio, lack of instructional materials, lack of infrastructure and facilities. From the spatial perspective, the education institutions are an unevenly distributed that students from the central part of the city are more advantageous. On the top of this, there are significant number of primary school students that travel more than 3km which is higher than the national standard. 64% of the total city boundary is accessible up to four (4) km. whereas 34% of the city boundary is beyond the maximum service area In this regard, in Gambela city 32% is well served, 20% is moderately served and 12 % is fairly serviced and the rest 34% is not accessible at all. This study also suggest that to improve the ongoing problems, in the first place government and stakeholder’s commitment is critical for the restoration of education quality. Secondly, planning, supervision and monitoring mechanisms for the entire education system should be in place. Thirdly, professional awareness particularly spatial planners’ must be increased so as to plan accessible and evenly distributed schools in master plan context. Fourthly, modern communication and information technology equipment have to be provided for each schools so as to bring transformations in the emerging regions. Keywords: Distribution; Emerging Region; Elementary; Gambela; Quality; School; Service Are