Browsing by Author "Mazimwe, Allan"

Now showing 1 - 8 of 8
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    Determination of Satellite-Derived PM2.5 for Kampala District, Uganda
    (Geomatics, 2022) Atuhaire, Christine; Gidudu, Anthony; Bainomugisha, Engineer; Mazimwe, Allan
    Ground monitoring stations are widely used to monitor particulate matter (PM2.5). However, they are expensive to maintain and provide information localized to the stations, and hence are limited for large-scale use. Analysis of in situ PM2.5 shows that it varies spatially and temporally with distinct seasonal differences. This study, therefore, explored the use of satellite images (Sentinel-2 and Landsat-8) for determining the spatial and temporal variations in PM2.5 for Kampala District in Uganda. Firstly, satellite-derived aerosol optical depth (AOD) was computed using the Code for High Resolution Satellite mapping of optical Thickness and aNgstrom Exponent algorithm (CHRISTINE code). The derived AOD was then characterised with reference to meteorological factors and then correlated with in situ PM2.5 to determine satellite-derived PM2.5 using geographically weighted regression. In the results, correlating in situ PM2.5 and AOD revealed that the relationship is highly variable over time and thus needs to be modelled for each satellite’s overpass time, rather than having a generic model fitting, say, a season. The satellite-derived PM2.5 showed good model performance with coefficient of correlation (R2) values from 0.69 to 0.89. Furthermore, Sentinel-2 data produced better predictions, signifying that increasing the spatial resolution can improve satellite-derived PM2.5 estimations.
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    An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda
    (ISPRS International Journal of Geo-Information, 2019) Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony
    One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish data in the disaster management sector using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess interoperability for datasets in the disaster management sector in Uganda and identify generic solutions to interoperability challenges in the context of disaster management. Semi-structured interviews and focus group discussions were used to collect qualitative data from sector stakeholders in Uganda. Data interoperability was measured to provide an understanding of interoperability in the sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for data in the sector. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder data integration and reuse in the sector. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing data interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem.
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    An Empirical Investigation of GIS Interoperability Best Practices In Industry
    (Preprints, 2018) Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony
    Reuse of patterns is a self-evident approach for managing interoperability concerns. Although patterns for resolving interoperability barriers exist in the literature, no study exists on adoption of interoperability patterns by Geographic Information Systems (GIS) practitioners in industry. Thus there is limited understanding of pattern re-usability, yet the advantages offered by interoperability patterns provide a reasonably sound justification for their usage. This paper examines the adoption of proven interoperability best practices in the GIS industry. An empirical study that involved the use of semi-structured interviews was employed to gather data from GIS developers on domain interoperability best practices. Results indicated that industry and communities of practice have been converging on the technical level to ensure interoperability of GIS concerns. Semantic interoperability and related patterns are least understood, yet semantic barriers still exist. This is partly due to the complexity associated with the top-down approach used to develop semantic interoperability solutions. Therefore, this study proposes research into resolving barriers in the adoption of interoperability patterns that reduce complexity while solving semantic interoperability barriers.
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    Implementation of FAIR Principles for Ontologies in the Disaster Domain: A Systematic Literature Review
    (International Journal o f Geo-Information, 2021) Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony
    The success of disaster management efforts demands meaningful integration of data that is geographically dispersed and owned by stakeholders in various sectors. However, the difficulty in finding, accessing and reusing interoperable vocabularies to organise disaster management data creates a challenge for collaboration among stakeholders in the disaster management cycle on data integration tasks. Thus the need to implement FAIR principles that describe the desired features ontologies should possess to maximize sharing and reuse by humans and machines. In this review, we explore the extent to which sharing and reuse of disaster management knowledge in the domain is inline with FAIR recommendations. We achieve this through a systematic search and review of publications in the disaster management domain based on a predefined inclusion and exclusion criteria. We then extract social-technical features in selected studies and evaluate retrieved ontologies against the FAIR maturity model for semantic artefacts. Results reveal that low numbers of ontologies representing disaster management knowledge are resolvable via URIs. Moreover, 90.9% of URIs to the downloadable disaster management ontology artefacts do not conform to the principle of uniqueness and persistence. Also, only 1.4% of all retrieved ontologies are published in semantic repositories and 84.1% are not published at all because there are no repositories dedicated to archiving disaster domain knowledge. Therefore, there exists a very low level of Findability (1.8%) or Accessibility (5.8%), while Interoperability and Reusability are moderate (49.1% and 30.2 % respectively). The low adherence of disaster vocabularies to FAIR Principles poses a challenge to disaster data integration tasks because of the limited ability to reuse previous knowledge during disaster management phases. By using FAIR indicators to evaluate the maturity in sharing, discovery and integration of disaster management ontologies, we reveal potential research opportunities for managing reusable and evolving knowledge in the disaster community.
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    Ontology Design Patterns for Representing Knowledge in the Disaster Risk Domain
    (IEEE, 2019) Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony
    The success of disaster risk management efforts depend on the ability of multiple stakeholders to share disasterrelated information. Semantic integration of such heterogeneous information requires ontology building. The top-down-approach of ontology building has several disadvantages to knowledge representation. To support the process of ontology engineering, a bottom-up-approach that utilizes modular Ontology Design Patterns (ODPs) with weak dependencies can be used to overcome the disadvantages of the top-down-approach. To bridge the availability gap of patterns for representing disaster knowledge, the study identifies existing and emerging patterns that can be used to organize disaster knowledge. Based on the eXtreme Design (XD) methodology and key informant interviews, Competency Questions (CQs) were listed from domain stakeholders. Consequently, corresponding patterns covering the CQs were also identified and developed. This study identifies emerging patterns such as Event Type ODP for representing risky and hazardous events. The QualityCausation ODP is also identified for representing the causality nature of vulnerability. The resulting patterns are aligned to the DOLCE1 foundational ontology and can be used to organize data in the disaster domain.
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    A Pattern Driven Approach to Knowledge Representation in the Disaster Domain
    (SN Computer Science, 2020) Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony; Barasa, Bernard
    Access to integrated disaster-related data through querying is still a problem due to associated semantic barriers. The disaster domain largely relies on the top–down approach of ontology development. This limits reuse due to associated commitments and complex alignments within ontologies. Therefore, there is a need to utilize a bottom-up approach that reuses patterns for representing disaster knowledge. To bridge the availability gap of patterns for representing disaster knowledge, this study identifies existing and emerging patterns for reuse while organizing disaster data from multiple sector stakeholders. Based on the eXtreme Design (XD) methodology and key informant interviews, competency questions (CQs) were elicited from domain stakeholders. The CQs are matched with existing patterns from other contexts. Emerging patterns (e.g the Event Classification and Quality Dependence Description for Objects) are also developed for CQs not captured and subsequently tested using SPARQL queries characterising the CQs. It is in this context that this paper presents a characterisation of disaster risk knowledge using CQs and corresponding patterns (reusable and emerging) covering the knowledge. Accordingly, we illustrate a pattern-driven use case to organise drought hazard data for early warning purposes. This provides a powerful use case for adopting a pattern-based approach to knowledge representation in the disaster domain.
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    Spatially explicit uncertainty modeling of zoonotic pathogen distribution: a case of Listeria monocytogenes in New York State, USA
    (Applied Geomatics, 2017) Mwima, Rita; Gidudu, Anthony; Mazimwe, Allan; Ligmann-Zielinska, Arika; Majalija, Samuel; Khaitsa, Margaret; Bergholz, Peter
    Listeria monocytogenes is a bacterium that is responsible for causing Listeria, a disease that has a wide range of adverse effects such as meningitis, bacteremia, complications during pregnancy, and other fatal illnesses especially among those whose immune systems are compromised. The purpose of this study was to establish hotspot candidate sites in New York State where the L. monocytogenes pathogen could be found. Several suitability criteria which include proximity to water, pasture, forests, and urban development and slope among others in New York State were considered in this analysis. This study assessed which spatial habitat factors influence habitat suitability of the L. monocytogenes pathogen in the forested areas of New York State. Multicriteria evaluation was used to integrate the different habitat factors using their different weights expressed using probability distributions. Spatially explicit uncertainty and sensitivity analysis (UA and SA) was carried out to examine the robustness of habitat suitability analysis. Suitability maps were generated and summarized using an average suitability map, a standard deviation uncertainty map, and sensitivity maps. Results showed that the shallowest depth to a wet soil layer (measured annually) and proximity to water are the habitat factors which contribute the most and individually to the distribution and survival of this pathogen. The slope gradient is singly insignificant but influential when associated with other factors like temperature, soil organic matter content, volume of water soil can store, proximity to forest, urban development, and pasture among others. It was established that water is the key habitat factor that favors the survival of this pathogen. Also, the ability to spatially model zoonotic pathogen hotspots is important in zoonoses control, informing and influencing policy. From these results, it is important to maintain the water quality of the water sources (lakes, rivers, ponds) and ensure that there is limited runoff from slopes.
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    Towards Interoperability: Has theoretical knowledge of Ontologies and Semantics had any impact on Geospatial Applications in GI Science?
    (IJTD, 2015) Mazimwe, Allan; Gidudu, Anthony
    The problem faced by Geographic Information Systems (GIS) today is the lack of interoperability among the various systems. Scientists do better when they share resources: computing power, data, tools, models, protocols, and results but making resources available is not the same as making them useful to others. Thus there is need to share common understanding of the structure of information among people or software agents, to enable reuse of domain knowledge, to make domain assumptions explicit and to automatically integrate disparate databases. This research focuses on how theoretical and conceptual research visions in the field of Ontologies and Semantics have impacted on spatial applications today. Using scholar search engines such as Web of Science, Google scholar, Research Gate and GI Science journals, a document review of ontology publications in GI Science was evaluated. Results showed a growing number in Ontology and Semantics publications in the geospatial domain since 1991 and that major research efforts have revolved around creation and management of geo-ontologies, ontology integration, and matching geographic concepts in web pages. Results further showed that ontologies and semantics have been used in SDI implementation, spatial databases, OGC web services, VGI, symbol grounding, semantic similarity, ´big’ Geodata and sensor networks, location based services, geocoding and so many other applications in the geospatial domain. This shows an evolution in different methods in representing multiple epistemological perspectives of same spatial events and entities as well as attaching contextual information in interest of enhancing interoperability across institutions and geography.