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18 results for “Dar es Salaam”
Dar Es Salaam Very-High-Resolution Land Cover Map
<p>This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively.</p> <p> The pixel values related to the following legend:</p> <p>5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 111=low elevated buildings</p> <p>The Out of Bag error of the product is 6,38% with the following class errors:</p> <p>Building = 0.035826</p> <p>Water = 0.049934</p> <p>Artificial Ground Surface = 0.077108</p> <p>Low Vegetation = 0.108709</p> <p>Tall Vegetation = 0.062278</p> <p>Bare Ground = 0.13803</p> <p>Shadow = 0.019872</p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p>This dataset was produced in the frame of REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé
<p>Primary data collected on Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé.</p>
Time Series Analysis of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>The study aimed to determine the existing linear influence levels of various causative factors of Urban Heat Island (UHI) by using Geographically Weighted Regression Model (GWR) that determines non-stationarity by generating a new equation for each sample size. Urban heat island occurs when higher temperatures reside in urban areas compared to surrounding areas due to the replacement of natural vegetation with construction materials during development purposes. Time Series with Linear Regression was used to determine the linearity between dependent and independent variables. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 were used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. In-situ datasets of the wind speed were acquired from Tanzania Meteorological Agency (TMA). UHI was observed to have a non-linear trend with IBI and wind speed and a linear trend with Albedo and EVI. The strongest values of UHI were observed at the city center, IBI was observed as a leading causative factor by having a non-lineality influence of 0.023 followed by Albedo, wind speed and EVI with an influence of 0.019, 0.016 and -0.015 respectively. Since IBI and Albedo contribute more to the development of heat island, urban residents should be encouraged to use construction materials with a lower absorption rate of solar energy.</p>
Shared decision making on mode of delivery following a prior cesarean delivery in Dar es Salaam, Tanzania
<p><strong>Background</strong>: Shared decision-making between clinicians and pregnant women with prior cesarean on the subsequent mode of delivery improves trial of labor rates, and reduces the number of repeat cesarean sections and their related complications. However, this practice is insufficient worldwide and the factors influencing it are still unknown. The study aimed at determining the proportion of pregnant women involved in shared decision-making and its associated factors in Dar es Salaam.</p> <p><strong>Methods</strong>: A cross-sectional analytical study among 350 pregnant women with one prior cesarean section. Data was collected using a structured questionnaire and SPSS 23 was used for analysis. A score of 80 or higher on the nine-item Shared Decision-Making Questionnaire (SDM-Q9) was used to calculate the proportion of women, and the associated factors were obtained using a logistic regression model. A p-value of < 0.05 was considered significant.</p> <p><strong>Results</strong>: The proportion of pregnant women involved in shared decision-making was 38%. Factors that were significantly associated with sharing decision-making were; having low level of education (AOR 0.55 95% CI 0.33–0.91), being married/having partner (AOR 2.58 95% CI 1.43–4.63), having a companion who had active participation (AOR 3.31 95% CI 1.03–10.6) and being familiar with the clinician (AOR 5.01 95% CI 1.30–19.2).</p> <p><strong>Conclusion</strong>: To promote the practice of shared decision-making in our setting, encouragement of socially vulnerable pregnant women's participation in decision-making by health care professionals, encouragement of companion participation during antenatal care and promotion of personal continuity of care to improve familiarity to clinicians are needed.</p>
Shared decision making on mode of delivery following a prior cesarean delivery in Dar es Salaam, Tanzania
Open the record for dataset details and reuse information.
Distribution. Five disjunct regions: Guinean and Sahelian savannas in W Africa (Guinea and Liberia E to Niger and Nigeria); White Nile valley (Sudan, South Sudan, and NE DR Congo); around Lake Victoria (Uganda, Kenya, and Tanzania); a narrow strip along the E African coast between Mombasa and Dar es Salaam; and SW shore of Lake Tanganyika in NE Zambia, based on a reidentification in 2010 of two specimens in the London Natural History Museum collected in 1920. in Molossidae
Distribution. Five disjunct regions: Guinean and Sahelian savannas in W Africa (Guinea and Liberia E to Niger and Nigeria); White Nile valley (Sudan, South Sudan, and NE DR Congo); around Lake Victoria (Uganda, Kenya, and Tanzania); a narrow strip along the E African coast between Mombasa and Dar es Salaam; and SW shore of Lake Tanganyika in NE Zambia, based on a reidentification in 2010 of two specimens in the London Natural History Museum collected in 1920.
The Impact Of Text Message (SMS) Reminders On Helmet Use Among Motorcycle Drivers In Dar Es Salaam, Tanzania
ClinicalTrials.gov study NCT02120742. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Etiologies of Fever Among Adults in Dar es Salaam
ClinicalTrials.gov study NCT01947075. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Microfinance and Health Intervention Trial for Youth in Dar es Salaam, Tanzania
ClinicalTrials.gov study NCT01865383. IPD Sharing: YES. Countries: 1. Publications: 8.
Distribution. Known only from type specimen at Dar-es-Salaam, EC Tanzania. in Vespertilionidae
Distribution. Known only from type specimen at Dar-es-Salaam, EC Tanzania.
Study to Investigate the Causes of Fever in Children Living in Urban Dar es Salaam and Rural Ifakara
ClinicalTrials.gov study NCT01812785. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of a Low-cost Warming Mattress Celsi Warmer for the Management of Hypothermic Newborns at Muhimbili National Hospital, Dar es Salaam, Tanzania
ClinicalTrials.gov study NCT05876884. IPD Sharing: NO. Countries: 1. Publications: 0.
Nails Versus Plates for Femur Fractures in Dar es Salaam, Tanzania
ClinicalTrials.gov study NCT01548456. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Experimental Design of Couple Counseling and Testing in Antenatal Clinics in Dar es Salaam, Tanzania
ClinicalTrials.gov study NCT00631384. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dar es Salaam Land Use and Informal Settlement Data Set
The Dar es Salaam Land Use and Informal Settlement Data Set represents urban land use and consolidation of informal settlements for the years 1982, 1992, 1998, and 2002, in Dar es Salaam, Tanzania. The land use categories are informal settlement areas, planned residential areas, ocean and estuaries, vacant and agriculture lands, and other urban features such as industrial or recreation areas. The data are based on the World Geodetic System spheroid of 1984 and use the Universal Transverse Mercator Zone 37 South projection.
Dar es Salaam
<p>capture </p>
Geographically Weighted Regression Modeling of a Nighttime Urban Heat Island in Dar es Salaam Metropolitan Areas
<p>Urban Heat Islands (UHI) is the urban microclimate with higher air temperature than surrounding areas. It is caused by both man-made and natural factors which vary geographically based on weather periods. To have a sustainable future in the environment, there is a need to regulate influence levels of various causative factors that generate UHI. Geographically Weighted Regression (GWR) model is among the spatial regression models that define the non-stationarity of variables. It generates a new equation on each sampled data unlikely global models like Ordinary Least Square (OLS). The study used GWR model to determine the influencing levels of three independent factors named Indexed-based Built-up Index (IBI), albedo and wind speed. Datasets were retrieved from MODIS satellite during the dry period of July from 2000 to 2019. IBI and Albedo were observed to have a strong negative influence with the maximum value of -0.045 and -0.053 respectively although, we expected to observe a positive influence on IBI since buildings emit absorbed energy during the night. Wind speed has a positive influence with the maximum value of 0.028 leading to the shift of heatwaves hence being termed as the secondary driving factor while IBI and Albedo as the primary driving factors. Wind speed is the highest driving factor that shifts emitted energies to other areas. We encourage an innovation in technology that produce higher albedo construction materials. We should improve environmental policies by introducing green cities through horizontal and vertical forests which might decrease the emitted energy into the atmosphere.</p>
Geographically Weighted Regression Modeling of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>Urban heat island is the phenomenon of having higher temperatures in urban areas compared to surrounding areas. It is caused by the replacement of natural vegetation with construction materials. Geographically Weighted Regression Model (GWR) determines non-stationarity among variables by generating a new equation for each sample size. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 are used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. The highest and lowest coverage of Urban Heat Island of 69% and 43% were observed in 2000 and 2005 respectively. IBI is the leading causative factor by having an influence of 0.023 followed by Albedo, wind speed and EVI with an average of 0.019, 0.016 and -0.015 respectively. We recommend innovation in producing higher albedo construction materials and introducing green city policies.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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