cmapit.studio is Launching a micro-grant for undergraduate research that use cmapit.io software as part of their methodology and analysis.
cmapit will provide micro-grant of N100,000 per student to 2 university undergraduates students working with cmapit studio as part of their research methodology and data analysis. The grantees will get 50% cash (after providing their budget) and also get training support the remaining 50% that will cover their travels and hotel accommodation.
To apply please share your research abstract as a comment below and Let your abstract reflect how you intend to work with cmapit software. The best two abstract will be selected.
Post by Udoh Blessing on Apr 26, 2019 13:06:27 GMT
PROJECT TOPIC: APPLICATION OF GIS TO GROUNDWATER VULNERABILITY STUDIES
ABSTRACT: Groundwater vulnerability is a measure of the ease with which contamination at the land surface can reach a productive aquifer. There are several man-made and natural factors that affect groundwater vulnerability. A good percentage of people living in urban areas make use of groundwater for their day to day activities hence, the importance of this study due to the increasing contamination/deterioration of ground water level, which poses a detrimental risk to the environment. For the identification of the level of this risk, extensive research has to be carried out on the study area. This paper will show the use of the DRASTIC method for vulnerability studies by using the Geographic Information System (GIS) tool CmapIt studio, to measure, analyse, scale and modify the DRASTIC parameters such as depth to the water table, net recharge , aquifer media, soil media, topography, impact vadose zone media, hydraulic conductivity of the aquifer in the study area. The input information such as borehole data, meteorological data, hydrological data, geology data, soil data, lithology data, contour map and topography map will be used to develop the GIS CmapIt studio data base. A final vulnerability map will be generated by integrating all the thematic maps of DRASTIC parameters through the Cmapit studio GIS environment. To indicate the relative degree of ground water vulnerability in the study area from high to medium to low.
A sample data is attached that shows how I used the CmapIt.io studio to create a heat map that shows the vandalized gas pipelines in the Niger Delta region of Nigeria for oil spill risk assessment.
Post by Samuelson Tijesunimi Atiba on May 14, 2019 22:01:15 GMT
DESIGN AND IMPLEMENTATION OF AN INTELLIGENT TRAVEL AID FOR THE VISUALLY IMPAIRED
ABSTRACT The visually impaired have little or no effective visual sensory input and have to rely on external assistance for navigation. Several electronic travel aids were developed to aid independent navigation of the visually impaired though these travel aids either offered some level of obstacle recognition or terrain analysis but not both. However, dangerous terrain features pose serious risks of hazard. This paper proposes the design and implementation of an Intelligent Travel Aid that combines the detection and recognition of objects with the analysis of terrain features for real-time identification of features that may pose a risk of hazard to visually impaired users. This system will use machine vision for object recognition and terrain feature detection. Two cameras for capturing object and terrain images respectively, a haptic device and a speaker connected to a Raspberry Pi development form the core of the system. The system will notify users of obstacles and terrain features via haptic feedback and synthesized speech. To visualize the terrain data and obstacle position in space during initial analysis and model fitting, cMapIT.io studio software will be employed. For real-time analysis of the surrounding environment, OpenCV will be used in conjunction with a Convolutional Neural Network running on an auxiliary Vision Processing Unit connected to the Raspberry Pi. The completion of this research work will help shape the future of assistive technologies to facilitate the independent navigation of visually impaired individuals. Further, the completed research work will result in the creation of an open image dataset relevant to the African context and so further inclusion in the development of artificial intelligence.
Post by Adebowale Daniel ADEBAYO on May 14, 2019 22:45:36 GMT
PROJECT TOPIC: SPATIAL ANALYSIS AND MAPPING OF LASSA FEVER AND ITS CONTRIBUTORY FACTORS IN ENDEMIC LGAS IN ONDO STATE.
ABSTRACT Lassa fever or Lassa Haemorrhagic Fever is an acute Viral Haemorrhagic Fever caused by the Lassa virus. In West Africa, the number of Lassa virus infections per year is estimated 100,000 to 300,000 number of cases and 5000 fatalities annually, and this is presently posing a risk to populations in both endemic and non-endemic locations in Africa. It was also reported that not less than 41 Local Government Area from 28 states in Nigeria and the Federal Capital Territory had witnessed the outbreak of Lassa Fever in the last five decades. Lassa Virus is being hosted by a species of rodent in the Muridae family known as “multimammate rats” (Mastomys natalensis). Mastomys rats are mostly found living in and around homes; more also scavenge on human’s leftover food or poorly stored food. The incubation period for Lassa Fever is within 6-21 days, with sorts of clinical signs and symptoms. The concept of spatial epidemiology embodies the spatiotemporal analysis of environmental factors, description, analysis of health data concerning demographic and socioeconomic factors and infectious contributory risk factors. This approach will be used in the analysis of the spatial data collected on the field to describe the nature and trend of the disease in the study area. Geographic Information System (GIS) which provides necessary tools for spatial and statistical assessment to enhance identification of the disease pattern will be used; most importantly Cmapit.io studio which can visualise and execute analytics for the health-related data acquired in the affected LGAs in the state.
Post by Arinze Chima Uzoezie on Jul 22, 2019 21:17:15 GMT
Application of GIS and remote sensing technique in census enumeration: A cases study of Housing Estate in Akpabuyo Local Government Area, Cross River State Abstract The application of Geographic Information System (GIS) and Remote Sensing (R.S) as an integrated decision support system tool for population enumeration has gained major recognition in the developed world, public and private sector and non-governmental organization such as the United Nation. Hence, this study aimed at identifying the contribution of satellite remote sensing (SRS) data and geographic information system (GIS) to the contingency planning, mapping and management of census (attributes) datasets in Nigeria, using CROSPIL Estate and Tari G Estate at Akpabuyo Local Government Area, Cross River State as case studies. The study utilized both primary and secondary sources of data collection, where the primary data were collected from enumeration exercise carried out by the researcher. While the secondary source of data, which was the satellite imagery of both study areas was retrieved using IKONOS-1m image data. In order to carryout the census survey, an Enumeration Area was first delineated for the both study areas. Ground reference data for carved-out area was collected using GPS hand set and the dataset was imported into the Cmapit.studio environment for geo-referencing of the satellite imagery. Next, the satellite imagery was digitized in Cmapit.studio environment in order to carve out the enumeration area for the census survey. Using an overlay grid of 3cm by 3cm, the researcher counted the number of buildings falling within each grid and multiply the numbers by the occupancy ratio estimated from the census survey to extrapolate the number of residents leaving in the estates. The result of the population survey using the remote sensing technique was compared with result obtained from the actual census carried out. It was discovered that there was no difference in the population estimate from remote sensing survey and census survey. Finally a growth rate of 3.2 % per year was calculated as the annual growth rate of the estate. Keywords: remote sensing; GIS; Cmapit; population census