Thesis and dissertation

Permanent URI for this collection

https://repository.suza.ac.tz/handle/123456789/38

Browse

Recent Submissions

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    AN ASSESSMENT OF URBAN GREEN SPACE USING MACHINE LEARNING MODEL FOR SUSTAINABLE DEVELOPMENT OF ZANZIBAR AT “URBAN WEST REGION
    (SUZA, 2022-12-01) HAMAD, ASHA A
    In this study we assessed the Urban Green Spaces (UGS) at the Urban West Region in Zanzibar using a machine learning-based method (Support Vector Machine) for the aim of classifying the land cover into five classes that include green non-green class (i.e. tree, shrub, grass, High dense buildup area, and low dense build-up area), identifying green connectedness and present reenery change from 2009 to 2081. UGS is very important in maintaining the attractive structure of the urban area which gives a lot of benefits to urban dwellers. In the Urban West Region of Zanzibar Green spaces cover a large area than the build-up area but are unevenly distributed however buildup areas are highly dense and congested on the west side of the Urban West Region.Approximately 0.019% of the area covered decreased from 2009 to 2009, this is due to the damage of the shoreline in a coastal zone of the east side of the Urban West Region. There is a high changing of trees, shrubs, and grass to low dense buildup areas, which eventually will be a high dense buildup area with a lot of problems such as increasing air pollution and mental health problem. Also, thematic maps obtained visualized green connectivity which is low, with the poor spatial pattern of green patches at the west of the Urban West Region due to the higher density of buildup area.Thus Urban Municipalities of Zanzibar should take a strong strategic plan for preserving and upgrading UGS for sustainable urban development in Zanzibar.