Christian Kwesi Owusu

The use of Geographical Information Systems (GIS) and Remote Sensing has become an increasingly important element in tertiary level in recent years across the world. Computer tools such as geographic information systems (GIS) and Remote Sensing have become fundamental basis for environmental assessment, regional planning, ecosystem conservation and natural resource management among others. GIS was first introduced into higher education in Ghana in 1998 as an undergraduate course in the department of Geomatic Engineering at the Kwame Nkrumah University of Science and Technology (KNUST). Although there exists a growing interest in these tools, both in the public and private institutions in Ghana, the implementation and usage of GIS and Remote Sensing is however less adopted in tertiary institutions in Ghana. GIS offer a wide range of possibilities in the fields of inventory making, planning and decision making. With GIS one can store and manage geographical data, integrate different types and analyze relations between data themes, perform spatial modeling, etc. The purpose of this paper is to provide an assessment of the use of GIS and the factors that encourage and discourage the adoption and implementation of GIS in tertiary institutions including University of Cape Coast (UCC, Cape Coast), University of Ghana (UG - Legon, Accra), and University of Energy and Natural Resources (UENR, Sunyani) with a pilot study in Kwame Nkrumah University of Science and Technology (KNUST, Kumasi). Although GIS and RS learning, adoption and its implementation has been recognized with various departments in the aforementioned institutions, however, the findings of the study also indicated that the adoption and impact of GIS learning and education has been hindered with various barriers. These barriers include technological barriers to the adoption of GIS, such as limited hardware and software, inadequate student access to computers, lack of interest in GIS learning, inadequate training and GIS lab, as well as inadequate mounting of appropriate GIS/Remote Sensing courses in tertiary institutions.

Road traffic accidents (RTAs) and its associated injuries are one of the major causes of death in developing countries. Some researchers blame illiteracy especially among commercial drivers while others mention the poor nature of the road as the cause of this menace. In recent years, road traffic accidents in Ghana have become a major social problem in the world as one writer put it " carriage without horses shall go and accident fill the world with woe ". The main objective of this study is to analyze the pattern of road traffic accidents in the Cape Coast Metropolis. Non-spatial/secondary data collection was used to obtain information on the location, the vehicle type, casualties, time and causes of the road accidents. One common feature is to assess information on RTAs. This study is therefore committed to explore issues surrounding road traffic accidents in Ghana with much emphasis on the Cape Coast Metropolitan area using Geographic Information System (GIS). Secondary data was used in obtaining the sample data. Data was collected directly from the record books of the MTTU in Cape Coast, a branch of the Ghana Police Service. Data was analyzed and interpreted with the aid of SPSS v. 21 and Arc GIS 10.3 versions. After the analysis, the study revealed that, 9 hotspots locations which were identified were comparatively competitive to each order. On a scale, the highest of all the hotspot intensity is Pedu Junction which recorded 33 cases followed by UCC East Gate which is responsible for about 30 counts. Morree Junction together with Nkanfoa/Third Ridge Junction recorded the same counts which are 22 each while Yamoransa Junction, Kotokuraba and Siwdu accounted for 21, 20 and 18 respectively. Kingsway and Abura recorded 15 occurrences each on a range. Finally, it was found out that most of the vehicles involved in road traffic accidents were private vehicles which numbered up to 232 cars making a share of (50.5%) out of the total of 459 vehicles. Hotspot locations and road traffic accident fatalities were mapped using GIS ArcMap/View.