| 1. |
- Sbarra, AN, et al.
(författare)
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Mapping routine measles vaccination in low- and middle-income countries
- 2021
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 589:7842, s. 415-
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Tidskriftsartikel (refereegranskat)abstract
- The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.
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| 2. |
- Aturinde, Augustus, et al.
(författare)
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Analysis of spatial co-occurrence between cancer and cardiovascular disease mortality and its spatial variation among the Swedish elderly (2010–2015)
- 2020
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Ingår i: Applied Geography. - : Elsevier BV. - 0143-6228. ; 125
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Tidskriftsartikel (refereegranskat)abstract
- CVD and cancer are the two leading causes of death worldwide. Improvement in cancer early detection and treatment has resulted in an increased number of cancer survivors. However, many of the survivors tend to develop CVD often leading to their demise. Conversely, people with pre-existing CVD conditions, especially the elderly, have increased chances of developing cancer and dying from the same. The World Health Organization, consequently, recommends joint management of both diseases. However, in Sweden, as with many other countries, few studies have explored the nature of the associations between the two disease mortalities and their spatial variation at a population level. This study uses correlation, global Moran's index and global bivariate Moran's index to investigate national trends of cancer and CVD crude mortality rates in the Swedish elderly. Spatial scan statistics, spatial overlay and local entropy maps were used to analyse for spatial co-occurrence, local joint spatial clustering and associations in the 2010–2015 cancer and CVD crude mortality rates for the Swedish elderly (65+ years). Mortality data were obtained from the Swedish Healthcare Registry. Our results showed that throughout the years of study, the correlation between cancer and CVD crude mortality rates was averagely positive. Spatial correlation analysis (univariate and bivariate) showed that the contribution of the neighbourhood mortality rates to the observed mortality rates was weak, though significant. From cluster analysis, the cancer and CVD crude mortality rates showed differences in clustering spatial scales with CVD clustering at a smaller scale. Finally, local entropy maps showed that cancer and CVD crude mortality rates were not always related across Sweden, but whenever they were, the relationship was mainly positive and linear. This study contributes to cancer and CVD public health efforts in Sweden by identifying areas where the two causes of death spatially co-occur, and where the two exhibit no spatial overlap. This provides a valuable starting ground for more focused studies to identify local drivers and/or informs coordinated targeted intervention in both causes of death.
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| 3. |
- Aturinde, Augustus, et al.
(författare)
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Space–Time Surveillance of COVID-19 Seasonal Clusters : A Case of Sweden
- 2022
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Ingår i: ISPRS International Journal of Geo-Information. - : MDPI AG. - 2220-9964. ; 11:5
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Tidskriftsartikel (refereegranskat)abstract
- While COVID-19 is a global pandemic, different countries have experienced different morbidity and mortality patterns. We employ retrospective and prospective space–time permutation analysis on COVID-19 positive records across different municipalities in Sweden from March 2020 to February 2021, using data provided by the Swedish Public Health Agency. To the best of our knowledge, this is the first study analyzing nationwide COVID-19 space–time clustering in Sweden, on a season-to-season basis. Our results show that different municipalities within Sweden experienced varying extents of season-dependent COVID-19 clustering in both the spatial and temporal dimensions. The reasons for the observed differences could be related to the differences in the earlier exposures to the virus, the strictness of the social restrictions, testing capabilities and preparedness. By profiling COVID-19 space–time clusters before the introduction of vaccines, this study contributes to public health efforts aimed at containing the virus by providing plausible evidence in evaluating which epidemiologic interventions in the different regions could have worked and what could have not worked.
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| 4. |
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| 5. |
- Dinc, Pinar, et al.
(författare)
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Fighting Insurgency, Ruining the Environment : the Case of Forest Fires in the Dersim Province of Turkey
- 2021
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Ingår i: Human Ecology. - : Springer Science and Business Media LLC. - 1572-9915 .- 0300-7839. ; 49:4, s. 481-493
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Tidskriftsartikel (refereegranskat)abstract
- Environmental destruction has long been used as a military strategy in times of conflict. A long-term example of environmental destruction in a conflict zone can be found in Dersim/Tunceli province, located in Eastern Turkey. In the last century, at least two military operations negatively impacted Dersim’s population and environment: 1937–38 and 1993–94. Both conflict and environmental destruction in the region continued after the 1990s. Particularly after July 2015, when the brief peace process that began in 2013 ended, conflict between the Turkish state and the Kurdistan Workers’ Party (PKK) resumed and questions arose about the cause of forest fires in Dersim. In this research we investigate whether there is a relationship between conflict and forest fires in Dersim. This is denied by the Turkish state but asserted by many Dersim residents, civil society groups, and political parties. We use a multi-disciplinary approach, combining methods of qualitative analysis of print media (newspapers), social media (Twitter), and local accounts, together with quantitative methods: remote sensing and spatial analysis. Interdisciplinary analysis combining quantitative datasets with in-depth, qualitative data allows a better understanding of the role of conflict in potentially exacerbating the frequency and severity of forest fires. Although we cannot determine the cause of the fires, the results of our statistical analysis suggest a significant relationship between fires and conflict in Dersim, indicating that the incidence of conflicts is generally correlated with the number of fires.
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| 6. |
- Farnaghi, Mahdi, et al.
(författare)
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Blockchain, an enabling technology for transparent and accountable decentralized public participatory GIS
- 2020
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Ingår i: Cities. - : Elsevier BV. - 0264-2751. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- Web-based public participatory GIS (PPGIS) has been used by governmental organizations to facilitate people's contribution to decision-making processes. However, these applications do not provide an open and transparent environment for public participation. This study suggests that PPGISs should be developed as decentralized applications (DApp) based on Ethereum blockchain technology to have a fully open, transparent, and accountable environment for public participation. In a blockchain-based PPGIS, the collected data are securely saved on the blockchain. The validity of the data, replicated on the nodes of the peer-to-peer blockchain network, is ensured through a consensus process without any central control. The data is tamper-free and immutable. Additionally, the data is openly accessible to institutions and citizens. A prototype PPGIS was developed as a DApp through which users can participate in the site selection of urban facilities. Using the application, they compare and rank different criteria. The system solves an analytic hierarchy process to calculate the weights of the criteria. A suitability map is generated afterward and published to be used by both citizens and decision-makers. The feasibility of the application, along with the issues that need to be considered while using blockchain technology for urban planning and development, are thoroughly discussed.
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| 7. |
- Farnaghi, Mahdi, et al.
(författare)
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Dynamic Spatio-Temporal Tweet Mining for Event Detection : A Case Study of Hurricane Florence
- 2020
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Ingår i: International Journal of Disaster Risk Science. - : Springer Science and Business Media LLC. - 2095-0055 .- 2192-6395. ; 11:3, s. 378-393
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Tidskriftsartikel (refereegranskat)abstract
- Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster. This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas. It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data. To precisely calculate the textual similarity, three state-of-the-art text embedding methods of Word2vec, GloVe, and FastText were used to capture both syntactic and semantic similarities. The impact of selected embedding algorithms on the quality of the outputs was studied. Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes. The proposed method was applied to a case study related to 2018 Hurricane Florence. The method was able to precisely identify events of varied sizes and densities before, during, and after the hurricane. The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed. The proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm, where it showed more promising results.
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| 8. |
- Guo, Zijian, et al.
(författare)
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Exploring the structural characteristics of intra-urban shared freight network and their associations with socioeconomic status
- 2023
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Ingår i: Travel Behaviour and Society. - : Elsevier BV. - 2214-367X. ; 32
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, shared freight systems have emerged in many cities as a new modality of freight transportation. However, little attention has been paid to the impact of a city's socioeconomic status on the characteristics of a shared freight network. To fill this gap, in this study, the structural characteristics of an intra-urban shared freight network are measured from the perspective of complex networks, and the correlations between network structure and socioeconomic status are examined. A case study is conducted in Hong Kong using large amounts of GPS trajectory data for freight vehicles and socioeconomic data. The results show that socioeconomic variables such as population size, percentage of elderly residents, percentage of residents with a marital status classified as “other” (i.e., separated, widowed, or divorced), and percentage of residents employed in the tertiary sector have distinct correlations with the structural characteristics. These correlations display spatial non-stationarity. This research can potentially assist decision-makers in improving the operating efficiency of shared freight systems and the governance of digital freight transport.
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| 9. |
- Hansson, Erik, et al.
(författare)
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An ecological study of chronic kidney disease in five Mesoamerican countries : associations with crop and heat
- 2021
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Ingår i: BMC Public Health. - : Springer Science and Business Media LLC. - 1471-2458. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Mesoamerica is severely affected by an epidemic of Chronic Kidney Disease of non-traditional origin (CKDnt), an epidemic with a marked variation within countries. We sought to describe the spatial distribution of CKDnt in Mesoamerica and examine area-level crop and climate risk factors.METHODS: CKD mortality or hospital admissions data was available for five countries: Mexico, Guatemala, El Salvador, Nicaragua and Costa Rica and linked to demographic, crop and climate data. Maps were developed using Bayesian spatial regression models. Regression models were used to analyze the association between area-level CKD burden and heat and cultivation of four crops: sugarcane, banana, rice and coffee.RESULTS: There are regions within each of the five countries with elevated CKD burden. Municipalities in hot areas and much sugarcane cultivation had higher CKD burden, both compared to equally hot municipalities with lower intensity of sugarcane cultivation and to less hot areas with equally intense sugarcane cultivation, but associations with other crops at different intensity and heat levels were not consistent across countries.CONCLUSION: Mapping routinely collected, already available data could be a first step to identify areas with high CKD burden. The finding of higher CKD burden in hot regions with intense sugarcane cultivation which was repeated in all five countries agree with individual-level studies identifying heavy physical labor in heat as a key CKDnt risk factor. In contrast, no associations between CKD burden and other crops were observed.
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| 10. |
- Huang, Weiming, et al.
(författare)
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Towards Knowledge-Based Geospatial Data Integration and Visualization : A Case of Visualizing Urban Bicycling Suitability
- 2020
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Ingår i: IEEE Access. - 2169-3536. ; 8, s. 85473-85489
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Tidskriftsartikel (refereegranskat)abstract
- Geospatial information plays an indispensable role in various interdisciplinary and spatially informed analyses. However, the use of geospatial information often entails many semantic intricacies relating to, among other issues, data integration and visualization. For the integration of data from different domains, merely using ontologies is inadequate for handling subtle and complex semantic relations raised by the multiple representations of geospatial data, as the domains have different conceptual views for modelling the geographic space. In addition, for geospatial data visualization - one of the most predominant ways of utilizing geospatial information - semantic intricacies arise as the visualization knowledge is difficult to interpret and utilize by non-geospatial experts. In this paper, we propose a knowledge-based approach using semantic technologies (coupling ontologies, semantic constraints, and semantic rules) to facilitate geospatial data integration and visualization. A traffic spatially informed study is developed as a case study: visualizing urban bicycling suitability. In the case study, we complement ontologies with semantic constraints for cross-domain data integration. In addition, we utilize ontologies and semantic rules to formalize geospatial data analysis and visualization knowledge at different abstraction levels, which enables machines to infer visualization means for geospatial data. The results demonstrate that the proposed framework can effectively handle subtle cross-domain semantic relations for data integration, and empower machines to derive satisfactory visualization results. The approach can facilitate the sharing and outreach of geospatial data and knowledge for various spatially informed studies.
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| 11. |
- Ismail, Kamukama, et al.
(författare)
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Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017
- 2021
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Ingår i: Geospatial health. - : PAGEPress Publications. - 1970-7096 .- 1827-1987. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.
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| 12. |
- Jeihouni, Mehrdad, et al.
(författare)
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Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management : a Novel Hybrid Use of Data Mining and GIS
- 2020
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Ingår i: Water Resources Management. - : Springer Science and Business Media LLC. - 0920-4741 .- 1573-1650. ; 34:1, s. 139-154
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision tree-based data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.
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| 13. |
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| 14. |
- Li, Aoyong, 1993, et al.
(författare)
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Comprehensive comparison of e-scooter sharing mobility: Evidence from 30 European cities
- 2022
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- Although e-scooter sharing has become increasingly attractive, little attention has been paid to a comprehensive comparison of e-scooter sharing mobility in multiple cities. To fill this gap, we conduct a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic. The comparisons are implemented from four perspectives, including temporal trip patterns, statistical characteristics (i.e., trip distance and duration), utilization efficiency, and wasted electricity during idle time. Results suggest that the similarity and difference co-exist between e-scooter sharing services in the cities, and utilization efficiency is significantly related with the number of e-scooters per person and per unit area. Surprisingly, on average nearly 33% of electricity are wasted during idle time in these cities. These research findings can be beneficial to further optimizing e-scooter sharing mobility services for transportation planners and micro-mobility operators.
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| 15. |
- Li, Aoyong, et al.
(författare)
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How did micro-mobility change in response to COVID-19 pandemic? : A case study based on spatial-temporal-semantic analytics
- 2021
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Ingår i: Computers, Environment and Urban Systems. - : Elsevier BV. - 0198-9715. ; 90
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Tidskriftsartikel (refereegranskat)abstract
- Cities worldwide adopted lockdown policies in response to the outbreak of coronavirus disease 2019 (COVID-19), significantly influencing people's travel behavior. In particular, micro-mobility, an emerging mode of urban transport, is profoundly shaped by this crisis. However, there is limited research devoted to understanding the rapidly evolving trip patterns of micro-mobility in response to COVID-19. To fill this gap, we analyze the changes in micro-mobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland. Specifically, docked bike, docked e-bike, and dockless e-bike are evaluated and compared from the perspective of space, time and semantics. First, the spatial and temporal analysis results uncover that the number of trips decreased remarkably during the lockdown period. The striking difference between the normal and lockdown period is the decline in the peak hours of workdays. Second, the origin-destination flows are used to construct spatially embedded networks. The results suggest that the origin-destination pairs remain similar during the lockdown period, while the numbers of trips between each origin-destination pair is reduced due to COVID-19 pandemic. Finally, the semantic analysis is conducted to uncover the changes in trip purpose. It is revealed that the proportions of Home, Park, and Grocery activities increase, while the proportions of Leisure and Shopping activities decrease during the lockdown period. The above results can help planners and policymakers better make evidence-based policies regarding micro-mobility in the post-pandemic society.
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| 16. |
- Masoumi, Zohreh, et al.
(författare)
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Dynamic urban land-use change management using multi-objective evolutionary algorithms
- 2020
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Ingår i: Soft Computing: A Fusion of Foundations, Methodologies and Applications. - : Springer Science and Business Media LLC. - 1432-7643. ; 24:6, s. 4165-4190
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Tidskriftsartikel (refereegranskat)abstract
- Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and assess the approach, a case study was conducted in Tehran, the capital of Iran. To satisfy the first step, four conflicting objective functions including maximization of consistency, maximization of dependency, maximization of suitability and maximization of compactness were defined and optimized using non-dominated sorting genetic algorithm. Per-capita demand was also employed as a constraint in the optimization process. Clustering analysis based on ant colony optimization was used to satisfy the second step. The results of the optimization were satisfactory both from a convergence and from a repeatability point of view. Furthermore, the objective functions of optimized arrangements were better than existing land-use arrangement in the area, with the per-capita demand deficiency significantly compensated. The approach was also communicated to urban planners in order to assess its usefulness. In conclusion, the proposed approach can extensively support and facilitate decision making of urban planners and policy makers in reforming and updating existing detailed plans after land-use changes.
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| 17. |
- Nakasi, Rose, et al.
(författare)
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A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country
- 2020
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Ingår i: Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020. - 2160-7516 .- 2160-7508. - 9781728193601 ; 2020-June, s. 4238-4244
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Konferensbidrag (refereegranskat)abstract
- Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor.
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| 18. |
- Ndagijimana, Albert, et al.
(författare)
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Childhood stunting is highly clustered in Northern Province of Rwanda : A spatial analysis of a population-based study
- 2024
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: In Northern Province, Rwanda, stunting is common among children aged under 5 years. However, previous studies on spatial analysis of childhood stunting in Rwanda did not assess its randomness and clustering, and none were conducted in Northern Province. We conducted a spatial-pattern analysis of childhood undernutrition to identify stunting clusters and hotspots for targeted interventions in Northern Province. Methods: Using a household population-based questionnaire survey of the characteristics and causes of undernutrition in households with biological mothers of children aged 1–36 months, we collected anthropometric measurements of the children and their mothers and captured the coordinates of the households. Descriptive statistics were computed for the sociodemographic characteristics and anthropometric measurements. Spatial patterns of childhood stunting were determined using global and local Moran's I and Getis-Ord Gi* statistics, and the corresponding maps were produced. Results: The z-scores of the three anthropometric measurements were normally distributed, but the z-scores of height-for-age were generally lower than those of weight-for-age and weight-for-height, prompting us to focus on height-for-age for the spatial analysis. The estimated incidence of stunting among 601 children aged 1–36 months was 27.1 %. The sample points were interpolated to the administrative level of the sector. The global Moran's I was positive and significant (Moran's I = 0.403, p < 0.001, z-score = 7.813), indicating clustering of childhood stunting across different sectors of Northern Province. The local Moran's I and hotspot analysis based on the Getis-Ord Gi* statistic showed statistically significant hotspots, which were strongest within Musanze district, followed by Gakenke and Gicumbi districts. Conclusion: Childhood stunting in Northern Province showed statistically significant hotspots in Musanze, Gakenke, and Gicumbi districts. Factors associated with such clusters and hotspots should be assessed to identify possible geographically targeted interventions.
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| 19. |
- Nduwayezu, Gilbert, et al.
(författare)
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Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest : A case study in Rwanda
- 2023
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Ingår i: Geospatial health. - 1970-7096. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.
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| 20. |
- Niyomubyeyi, Olive, et al.
(författare)
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A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning
- 2020
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Ingår i: Algorithms. - : MDPI AG. - 1999-4893. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.
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| 21. |
- Niyomubyeyi, Olive, et al.
(författare)
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An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda
- 2024
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Ingår i: Geo-Spatial Information Science. - : Informa UK Limited. - 1009-5020 .- 1993-5153. ; , s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali, Rwanda. Efficient Non-dominated Sorting (ENS) algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions, and local search ability, and to accelerate the convergence speed of the algorithm. The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO. Furthermore, the proposed algorithm could generate optimal land use scenarios according to the preferred objectives, thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.
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| 22. |
- Olsson, Per-Ola, et al.
(författare)
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Exploring the potential to use in-between pixel variability for early detection of bark beetle attacked trees
- 2023. - 35
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Ingår i: 26th AGILE Conference on Geographic Information Science “Spatial data for design”. ; 4:1
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Konferensbidrag (refereegranskat)abstract
- The European spruce bark beetle (Ips typographus L.) is a major disturbance agent in Norway spruce (Picea abies (L.) Karst) forests in Europe and it is estimated that a changing climate will result in more severe outbreaks in the future. To reduce the risk of large outbreaks it is important to have methods that enable early detection of bark beetle attacks to help forest managers to prevent population build-up, e.g by sanitary cutting. Several studies have been devoted to early detection of bark beetle attacks with Sentinel-2 data with a focus on spectral properties and vegetation indices for early detection with pixel-based methods. In this study we explore the potential to use changes in variability between pixels in windows of different sizes (3×3, 4×4 and 5×5 pixels). We compute the coefficient of variation for four vegetation indices (NDVI, NDWI, CCI and NDRS) in a time-series of Sentinel-2 data during a bark beetle outbreak in Sweden that was triggered by a drought in 2018. The results indicate that CCI is the most promising index for early detection and that the variability between pixels increase in windows with attacked trees from late July when the main swarming was the second week of May.
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| 23. |
- Omidipoor, Morteza, et al.
(författare)
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Knowledge discoveryweb service for spatial data infrastructures
- 2021
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Ingår i: ISPRS International Journal of Geo-Information. - : MDPI AG. - 2220-9964. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge DiscoveryWeb Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.
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| 24. |
- Pilehforooshha, Parastoo, et al.
(författare)
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A new model combining building block displacement and building block area reduction for resolving spatial conflicts
- 2021
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Ingår i: Transactions in GIS. - : Wiley. - 1361-1682 .- 1467-9671. ; 25, s. 1366-1395
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Tidskriftsartikel (refereegranskat)abstract
- Applying pre-processing and geometric transformation, for the generalization of building blocks, can lead to spatial conflicts which are mainly resolved by displacement. However, the conflicts may not be resolved, due to the insufficient space for displacement or the existence of other objects that prevent displacement. This article proposes a novel model combining building block displacement and building block area reduction for resolving spatial conflicts. In this model, first the immune genetic algorithm with improved objective function, with the goal of minimizing the total conflicting area, is used for displacement. Second, a building block area reduction model is applied to reduce the area of the unresolved conflicting building blocks. For this, a building segment partitioning model is developed for partitioning the boundaries of conflicting building blocks. Then, the conflicting segments are locally simplified for more adaptation to the initial generalized ones. We generalized two datasets from scale 1:25 to 1:50k and then used the datasets with scale 1:50k to assess the proposed model. The results demonstrate that the proposed model has improved the correctness and completeness by 3.62 and 4.24% for dataset 1 and 5.67 and 7.92% for dataset 2, respectively, compared to one of the recent models for resolving conflicts.
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| 25. |
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| 26. |
- Sekulic, Milan, et al.
(författare)
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Multi-criteria spatial-based modelling for optimal alignment of roadway by-passes in the Tlokweng planning area, Botswana
- 2022
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Ingår i: Journal of Spatial Science. - : Informa UK Limited. - 1449-8596 .- 1836-5655. ; 67:2, s. 237-254
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Tidskriftsartikel (refereegranskat)abstract
- In this study, Spatial Multi-Criteria Evaluation and the least cost path analysis were applied to find the optimal by-pass road alignment in the Tlokweng Planning Area in Botswana. One-At-a-Time sensitivity analysis and the statistical test for zero proportion were used to investigate the robustness of the entire model. Four alternative by-pass roads were produced stressing economic, environmental, and social suitability as well as trade-offs between the groups. The results showed that the social alternative performs best. Sensitivity analysis and statistical test for zero proportion revealed four criteria as sensitive.
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| 27. |
- Sicuaio, Tomé, et al.
(författare)
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Sustainable and Resilient Land Use Planning : A Multi-Objective Optimization Approach
- 2024
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Ingår i: ISPRS International Journal of Geo-Information. - 2220-9964. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience.
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| 28. |
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| 29. |
- van Loenen, Bastiaan, et al.
(författare)
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SPIDER : Open spatial data infrastructure education network
- 2020
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Ingår i: CEUR-WS. - 1613-0073. ; 2797, s. 355-358
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Konferensbidrag (refereegranskat)abstract
- In this 2 hour workshop the experiences of the geographic data domain will be shared with the open data research & education community to promote and strengthen active innovative learning and teaching in both worlds. The domain of geographic data can be considered as one of the front running in open data. Over the past two decades, many geographic datasets in Europe became available as open data through the open [spatial] data infrastructure. Several of the high value dataset categories in the EU Directive on Open data and reuse of Public Sector Information have a geographic component. Teachers in this domain are struggling with the concepts of data ecosystems and data infrastructures presented in the academic literature. A very current discussion is on the exact scope of 'open' spatial data infrastructures (SDIs) (see Vancauwenberghe et al. 2018), in which also nongovernment data and nongovernment actors should be considered as key to the performance of the infrastructure and/or ecosystem. Moreover, teaching methods are still limited to traditional teaching in the classroom. As a consequence, there is barely an international exchange of educational material and approaches on open SDI among universities. In this workshop an overview and detailed analysis of the concepts of open data ecosystems and infrastructures are presented and discussed and existing open data education highlighting good practices of learning, teaching and training in open [spatial] data infrastructures or ecosystems explored.
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| 30. |
- Zhao, Pengxiang, et al.
(författare)
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A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management.
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| 31. |
- Zhao, Pengxiang, et al.
(författare)
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Impact of data processing on deriving micro-mobility patterns from vehicle availability data
- 2021
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Vehicle availability data is emerging as a potential data source for micro-mobility research and applications. However, there is not yet research that systematically evaluates or validates the processing of this emerging mobility data. To fill this gap, we propose a generally applicable data processing framework and validate its related algorithms. The framework exploits micro-mobility vehicle availability data to identify individual trips and derive aggregate patterns by evaluating a range of temporal, spatial, and statistical mobility descriptors. The impact of data processing is systematically and rigorously investigated by applying the proposed framework with a case study dataset from Zurich, Switzerland. Our results demonstrate that the sampling rate used when collecting vehicle availability data has a significant and intricate impact on the derived micro-mobility patterns. This research calls for more attention to investigate various issues with emerging mobility data processing to ensure its validity for transportation research and practices.
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| 32. |
- Zhao, Pengxiang, et al.
(författare)
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Impact of transit catchment size on the integration of shared e-scooters in the public transport system
- 2024
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Ingår i: Proceedings of the 27th AGILE Conference on Geographic Information Science. ; 56
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Konferensbidrag (refereegranskat)abstract
- E-scooter sharing has been commonly used to integrate public transport systems in many cities worldwide. Accurately modeling integration between shared e-scooters and public transport is important for multi-modal urban transportation development and management. However, the effects of catchment size are scarcely considered while modeling their integration by widely adopting the method based on the transit catchment area in previous studies. In this paper, we systematically quantify the impact of the size of the transit catchment area on the integration of shared e-scooters in the public transport system from statistical, temporal, and spatial perspectives. A case study is implemented in Stockholm, Sweden. The results indicate that the transit catchment size has a significant impact on their integration, especially on spatial patterns. This research calls for more attention to consider such catchment size effects to ensure the validity of integration results for urban mobility research and practice.
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| 33. |
- Zhao, Shuangming, et al.
(författare)
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How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity : a rapid assessment incorporating with satellite-derived information and urban mobility data
- 2024
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Ingår i: International Journal of Health Geographics. - 1476-072X. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. Methods: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers’ exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers’ activities. Results: The taxi drivers’ weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO’s recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the “Inner Ring Road”, while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the “Third Ring Road”. Conclusion: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.
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