Spatial and household perception survey data on green spaces security in Sunyani Municipality in Ghana
Description
The data on a household perception survey and geospatial data were acquired to study the security of green spaces in the Sunyani Municipality of Ghana. The household survey data is mainly on household characteristics such as gender, age, employment, educational attainment, years of stay in the community and residential status among others. The spatial data is on Landsat images acquired for the study area for the years 1989, 2003, 2013 and 2023. It includes data on the accuracy assessment of the Landsat images
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Steps to reproduce
The spatial data was acquired through the United States Geological Survey Department. The images were processed using Remote Sensing techniques and classified with Maximum Likelihood Classification Algorithm (MLCA) using ArcGIS 10.8 and QGIS 3.30 ArcMap's Equalized-Stratified Sample Algorithm (ESSA) with Google Earth Pro Engine was employed to ground truth the points for the land use accuracy assessment. The assessment was done using the user, producer, and Kappa coefficients. The measurements were computed using ArcMap's Confusion Matrix Algorithm (CMA). The household data was acquired through a structured questionnaire. The sample size was determined through simple random sampling using Yamane sample formulae. Proportional sampling was applied to each study community to determine the sample size for each. Then, simple random sampling was used to select the houses to survey. In Sunyani, neighbourhood clustering was used and then after simple random to select the houses. In each house, one household member 18 years old was selected using convenient sampling. The data was then entered into SPSS 23. The institutional interviewees were purposively selected based on the core competencies and the interviews were conducted with the help of a semi-structured questionnaire. The interviews were tape-recorded with the consent of the interviewees and later transcribed and coded with Taguette and QDA Lite.