Edited Conference Papers

Ghana’s economy accelerated to 8.1 % in 2017, driven by the mining and oil sectors, making it the fastest growing African economy, trailing only Ethiopia. Industry’s growth is expected to improve to 9.7 % in 2019. These growth rates are expected to translate into the improvement and growth of Industry and infrastructure (SDG 9), which requires large tracts of land. The growth of the mining sector also requires large tracts of agricultural lands. However, 80 % of the land in Ghana are customarily owned. This requires government to compulsorily acquire land for public good to improve life on land (SDG 11 & 15). Government must also pay fair and adequate compensation to land losers as required by the Constitution of Ghana. Over the years, these acquisitions have been met with violence, tension and litigations due to unfair, inadequate and non-transparent compensations. When compensations are paid, they are delayed due to data acquisition and processing challenges. This promotes inequity, loss of livelihood and poverty. This study set out to identify compensable agricultural resources by applying modern geo-spatial technologies to create a geo-database of major resources in the sixteen regions in Ghana. A Cost Centre and “linear regression model” was generated by adequately costing all the elements for developing the resources. The process was inclusive of major stakeholders and was scientifically done in accordance with best practice. The computerised model package, COMPACAL-G was developed to ensure fair, adequate and transparent compensation that promotes equity (SDG 10), ensures sustainable livelihood for vulnerable farmers and mitigates poverty (SDG 1). COMPACAL-G ensures faster data acquisition, processing, assessment of fair, transparent and prompt compensation calculation, which the present system fails to address. COMPACAL-G can be used to undertake desk top preliminary assessment of possible compensation figures prior to acquisitions. This enables compensation to be adequately factored into project costs at the inception stage.

The use of Unmanned Aerial Vehicles (UAVs) for remote data acquisition has rapidly evolved in recent years. The integration of UAV with Global Positioning System (GPS) and Geographic Information System (GIS) techniques have reduced time and cost in acquiring data for inaccessible land areas. Topographical mapping of water bodies, marshy areas or land areas without land cover using traditional methods of surveying in Ghana is time consuming and challenging. This study considered topographical mapping of muddy Tailings Storage Facility (TSF) sites at a rain forest mining area at Osino in the Eastern Region of Ghana. DJI Phantom 4 Pro consumer UAV was flown at an altitude of 75m in a 3D flight mode. Real Time Kinematic (RTK) GPS was used to coordinate a reflective-marked Ground Control Point (GCP). Georeferencing the orthophoto was done using the GCP. Digital Terrain Model (DTM) was generated from the processed orthophotos after which contours at 5 m interval were generated. Cross sections across the TSF were drawn for further geotechnical and stability analysis for the tailings dam. The accuracy of the topographic map is below 5 cm and confirms the suitability of using consumer grade UAVs for topographical mapping of inaccessible areas in a cost-effective manner. Thus, the integration of RTK technology with the UAV and GIS is a feasible and appropriately accurate solution for mapping inaccessible areas.

This paper analyzes the influence of personal/family car ownership and ability to drive on the use the public transport services in the Cape Coast Metropolis with a view to helping in planning all efficient and effective transport system to boost the local economy. It deployed a cross-sectional descriptive design to develop a nuanced understanding of the complexities involved in the relationship between car ownership and ability to drive on the one hand, and the use public transport on the other hand. A survey was carried out on users of public transport users (bus (transports (n = 309) using a mobile phone application “Kobocollect” to gather the data. The findings revealed that the majority of the respondents used taxis and used it twice daily, own a personal/family car and can’t drive, walked to the terminal, spent 5-10 minutes invehicle waiting time and 10-15 travel time. The study further showed that there was a statistically significant association between ownership of personal/family car and the frequency of public transport use. Also, there is a statistical association with the ability to drive and the use of public transport.