Green Spaces in Residential Areas of Dar es Salaam City: Types, Coverage and Uses
Bibliographic record
Abstract
Green spaces in urban areas including in Dar es Salaam City provide multiple ecological, social and economic benefits. Despite their benefits they are inadequately documented in terms types, coverage and uses. This paper attempts to provide information on types, coverage and uses of green space in Dar es Salaam City. A number of methods including literature review, interpretation of remotely sensed image, interviews, focus group discussions and questionnaires were used to document city greenery. The research findings show that residential greenery is made up of greenery found within and external to plots. The dominant green spaces external to residential plots were natural and semi natural vegetation while within plots were woody plants, plots farms vegetable and ornamental gardens. Distribution of greenery varied among the wards due to differences in building density and distance from the city centre. Natural and semi natural vegetation increased with decrease of building density and increase of distance from the city centre, while the number of plots with trees for shade increased with increase of building density. Only Kawe ward that had greenery above Tanzania space planning standards, the other three wards which are informal settlements had green space deficit. Three quarters of the households use green spaces for shade provision and cooling, two thirds as a source of food products and a quarter for recreation and aesthetic purposes. The study reveals that Dar es Salaam City residents invest predominantly on shade trees in their residential plots compared to other green space types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".