This study analyzed trend and examined the factors responsible for urban sprawl in Akure with a view to develop a user-friendly geospatial database for monitoring urban sprawl in the study area. Medium resolution satellite imageries derived from Landsat (TM) and (ETM+) comprising of four dates (1986, 1991, 2002 and 2011) were analyzed. The results revealed that built-up area increased rapidly by 43.31% from 5857.54 hectares in 1986 to 8394.21 hectares in 1991. It further increased by 72.02% from 8762.76 hectares to 15073.7 hectares in 2011. Field study conducted in 2013 involved in-depth interviews and questionnaires to stakeholders and residence respectively. The analysis showed that there was a weak negative relationship (r = -0.189, p < 0.01) between gender and “house ownership”, a weak positive relationship (r = 0.343, p < 0.01) between marital status and “house ownership” and a weak negative relationship (r = -0.159, p < 0.05) between “number of children” and “house ownership” in the sprawl location. Geo-spatial database modeled was tested by subjecting it to spatial analysis to show its capability to answer question pertaining to all the entities of the database. The study concluded that urban sprawl increased and if not reversed, might constitute greater social and environmental problems in the future.
Urban Sprawl is unplanned, uninterrupted monotonous developments that does not provide for a functional mix of uses and which variously appears as low-density, ribbon or strip, scattered, leapfrog discontinuous development and inefficient use of land [
Population increases, so do the needs for new facilities, amenities and decentralization from the urban core and precious farmland is often left unprotected from commercial or residential developers [
The use of geospatial technique to assess urban sprawl cannot be over emphasised. The data derived from these techniques and its analysis provides new insights into the interaction of geographic phenomena [
The share of population living in urban areas increased from 20.2% in 1971 to 23.7% in 1981 and to 26.1% in 1991 [
Akure, the state capital of Ondo State, lies between latitudes 7˚12'N, 7˚19'N and longitudes 5˚08'E, 5˚18'E (
has been on the increase from 230,000 in 1971 to 1.18 million in 2007 [
During the field study a handheld Global Positioning System (GPS) with an accuracy level of +/−10 m was used to identify the coordinate of features and training samples in the study area which were integrated into the GIS environment. In-depth interview was carried out to capture information from the officers in charge of urban development in “Ondo State Ministry of Housing and Urban Development” and “Akure South Urban and Regional Planning Office”. This was to solicit information based on their years of experiences on the identification, problems, impacts and factors responsible for urban sprawl and also the management/monitoring of the sudden growth. The sample size for the in-depth interview was 100% population which was all the Directors in the Ministry of Housing and Urban Development. Another 100% sample size was taken in the Local Government office in Akure Area Office of Urban and Regional Planning. Therefore, a total of thirteen officers were interviewed. Questionnaires were administered to assess the perception of the populace on the trend and impact of urban sprawl using the Slovin principle, [
Sample population = N 1 + N e 2 (1)
where N = total targeted population, e = confidence level (0.05 for 95%).
Stratified random sampling technique was used to administer the questionnaire. The division was based on the number of wards with sprawl location(s), subsequently, only fewer than 200 copies of questionnaire were returned.
Satellite imageries (Landsat TM 1986, 1991, ETM+ 2002, 2011) of the study area were acquired from Land Satellite Programme of the National Aeronautics and Space Administration (NASA), (http://landsat.gsfc.nasa.gov/). The satellite imageries were subjected to digital image processing (DIP) and each band of the imageries was filtered using the 3 × 3 median filter. This was done to reduce fragmentation and to clearly identify the various land cover in the study area after [
0 i , j = I i − 1 , j − 1 ∗ K 1 + I i − 1 , j ∗ K 2 + ⋯ + I i + 1 , j + 1 ∗ K 9 (2)
where i is the row, j is the column and K is the filter kernel.
Maximum likelihood classifier was the algorithm used to classify the imageries. This was done by training pixel based on multivariate probability density function (pdf) of the classes of interest [
p ( X | ω c ) p ( c ) ≥ p ( X | i ) p ( i ) (3)
where X: the spectral multivariate vector;
p ( X | ω c ) : pdf of X, given that X is a member of class c;
p(c): a priori probability of class c in the image;
i: class number among the m number of classes in the image.
The classified imageries were then subjected to accuracy assessment. Stratified random sampling procedure was adopted for assessing the overall accuracy assessment [
Also, geospatial techniquesoperations were used in this study which is the GIS operations of Overlay, Query and search and GIS Unique Identifier. Correlation (Matrix) Analyses wasalso used to analyze data generated from the questionnaires administered at the identified sprawl locations. This was modeled after [
Seven bands were used for the colour composite map on Landsat TM 1986 and 1991, divided into three bands in visible spectrum, one in the near-infrared, two, in mid-infrared and one in Thermal-infrared. These bands were selected because
of the sharp contrast the combination produced. The same combination was used for Landsat ETM+ (437), where band 4 is placed on Red, 7 on Green and 3 on blue from the spatial and spectral image characteristics.
use cover change. Analysis indicated that the Built up area had increased rapidly from 5857.54 hectares in 1986 to 8394.21 hectares in 1991 accounting for 43.31%; it then further increased to 8762.76 hectares in 2002 by 4.39% and to 15073.7 hectares in 2011 by 72.02%. The overall accuracy assessment of the classification for Landsat TM of 1986 was 88%, while that of Landsat TM 1991 was 88%. Landsat ETM+ of 2002 and 2011 had an overall accuracy assessment of 92% and
Land use Types | 1986 | 1991 | 2002 | 2011 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Area (hectares) | Area (%) | Area (hectares) | Area % | Rate of Change (%) 1986-1991 | Area (hectares) | Area % | Rate of Change (%) 1991-2002 | Area (hectares) | Area % | Rate of Change (%) 2002-2011 | %Land Cover Change 1986-2011 (hectares) | |
Built-up Area | 5857.54 | 8.61 | 8394.21 | 12.33 | 43.31 | 8762.76 | 12.87 | 4.39 | 15073.7 | 22.14 | 72.02 | 157.34% |
Agro-Forest | 57958.1 | 85.18 | 46913.5 | 68.91 | −19.06 | 37508.9 | 55.09 | −20.05 | 32574.9 | 47.85 | −13.15 | −43.80% |
High Forest | 3311.71 | 4.87 | 7158.78 | 10.52 | 116.17 | 20272.9 | 29.78 | 183.19 | 19606.5 | 28.80 | −3.29 | 492.04% |
Water Body | 916.299 | 1.35 | 5615.37 | 8.25 | 512.83 | 1537.29 | 2.26 | −72.62 | 826.83 | 1.22 | −46.22 | −9.76% |
Total | 68,043 | 100.00 | 68,081 | 100.00 | 68,081 | 100.00 | 68,081 | 100.00 |
Source: Landsat TM 1986; 1991 and Landsat ETM+ 2002; 2011. Fieldwork, 2014.
84% respectively. Stratified random point generator was used to get 50 random points on each image for the accuracy assessment from Erdas Imagine Software with their coordinates. The assessment was performed through Field work: Topographical map, Google Earth and personal knowledge of the area really assisted in validating the classification with the use of GPS.
These findings proved that Akure had grown overtime. Also, the results of the image classification showed that in 1986, the area covered by water body was 1.35% (916.299 hectares); this increased to 8.25% (5615.37 hectares) in 1991 (an interval of 5 years). It was observed that the initial increase was attributed to the degrading of the forest lands as a result of human activities thereby exposing more water body that existed in the forest. This water body in 1991 that covered 8.25% decreased to 2.26% in 2002 (an interval of 11 years). This is because the exposed water body was now easily available for human activities and other climatic and environment factors to use it up.
Vector data for the Built-Up Area for the four years were extracted from the satellite image and overlaid with 1986 as the base year (
Landsat (TM) 1986, 1991; (ETM+) 2002 and 2011 imageries used in this study showed that the Built-up area had undergone serious increment between the period of 25 years (1986-2011). The Built-up area increased from 8.61% occupying an area of 5857.54 hectares in 1986 to 22.14% occupying an area of 15073.7 hectares in 2011. [
The assessment and analysis of the 1986 to 2011 satellite imageries of the area revealed that there was a decline in the Agro-Forest from 85.18% covering 57958.1 hectares in 1986 to 47.85% covering 32574.9 hectares in 2011 while High Forest increased to 2002 and then decline. [
Field data was acquired and integrated with the satellite image data to support its findings on the urban sprawl. Field data also confirmed that Akure had undergone urban sprawl by assessing its factors and impacts. It was discovered from the field observation that the establishment of Akure as the Ondo State Capital attracted various social and infrastructural facilities that further attracts people from different states and local governments for employment, business and education. The relationship between house ownership in the sprawl location and demographic information of the respondents was conducted using inferential statistical tool of Correlation analysis. The variables tested include house ownership in the sprawl location, gender, age, marital status, level of education, number of children, tribe, and changing occupation.
Findings from this study revealed the trends and factors responsible for urban sprawl between 1986-2011. The analysis of Landsat (TM) 1986, 1991; (ETM+) 2002 and 2011 imageries showed the trends of sprawling in Akure between the period of 25years (1986-2011). According to
Correlation matrix of the house ownership in the sprawl location and the personal information of the respondents | |||||||||
---|---|---|---|---|---|---|---|---|---|
Do you own a house around this area? | Gender | Age | Marital status | Level of education | No. of children | Tribe | Have you changed occupation? | ||
Do you own a house around this area? | Correlation Coefficient | 1.000 | −0.189** | −0.131 | 0.343** | −0.079 | −0.159* | 0.051 | 0.085 |
Sig. (2-tailed) | 0.008 | 0.066 | 0.000 | 0.273 | 0.029 | 0.478 | 0.248 | ||
N | 197 | 197 | 197 | 197 | 197 | 190 | 197 | 188 | |
Gender | Correlation Coefficient | −0.189** | 10.000 | −0.265** | −0.040 | −0.006 | −0.026 | 0.039 | 0.012 |
Sig. (2-tailed) | 0.008 | 0.000 | 0.574 | 0.933 | 0.723 | 0.585 | 0.870 | ||
N | 197 | 200 | 200 | 200 | 200 | 193 | 200 | 191 | |
Age | Correlation Coefficient | −0.131 | −0.265** | 10.000 | 0.336** | 0.106 | 0.185* | −0.015 | 0.205** |
Sig. (2-tailed) | 0.066 | 0.000 | 0.000 | 0.136 | 0.010 | 0.830 | 0.004 | ||
N | 197 | 200 | 200 | 200 | 200 | 193 | 200 | 191 | |
Marital status | Correlation Coefficient | 0.343** | −0.040 | 0.336** | 10.000 | 0.007 | 0.045 | 0.024 | −0.008 |
Sig. (2-tailed) | 0.000 | 0.574 | 0.000 | 0.917 | 0.530 | 0.731 | 0.913 | ||
N | 197 | 200 | 200 | 200 | 200 | 193 | 200 | 191 | |
Level of education | Correlation Coefficient | −0.079 | −0.006 | 0.106 | 0.007 | 10.000 | 0.337** | 0.378** | −0.167* |
Sig. (2-tailed) | 0.273 | 0.933 | 0.136 | 0.917 | 0.000 | 0.000 | 0.021 | ||
N | 197 | 200 | 200 | 200 | 200 | 193 | 200 | 191 | |
No. of children | Correlation Coefficient | −0.159* | −0.026 | 0.185* | 0.045 | 0.337** | 1.000 | 0.353** | −0.114 |
Sig. (2-tailed) | 0.029 | 0.723 | 0.010 | 0.530 | 0.000 | 0.000 | 0.120 | ||
N | 190 | 193 | 193 | 193 | 193 | 193 | 193 | 186 | |
Tribe | Correlation Coefficient | 0.051 | 0.039 | −0.015 | 0.024 | 0.378** | 0.353** | 10.000 | −0.275** |
Sig. (2-tailed) | 0.478 | 0.585 | 0.830 | 0.731 | 0.000 | 0.000 | 0.000 | ||
N | 197 | 200 | 200 | 200 | 200 | 193 | 200 | 191 | |
Have you changed occupation? | Correlation Coefficient | 0.085 | 0.012 | 0.205** | −0.008 | −0.167* | −0.114 | −0.275** | 10.000 |
Sig. (2-tailed) | 0.248 | 0.870 | 0.004 | 0.913 | 0.021 | 0.120 | 0.000 | ||
N | 188 | 191 | 191 | 191 | 191 | 186 | 191 | 191 |
**means significant at 0.01. *means significant at 0.05.
work revealed through correlation matrix analysis that gender, marital status and number of children are significantly responsible for urban sprawl in Akure. This submission was related to the findings of [
Database was created with features of each layer stored in separate table as feature class which were later combined together to form a comprehensive Database. The feature classes are Local Government, Wards, Road network, Water bodies, Settlement. All these are linked to the base table to form the Monitoring Urban Sprawl (MUS) Database.
Overlay operation places two spatial features on each other based on spatial reference to give their spatial locational relationship. In this work, the built-up areas of the four satellite imageries (1986-2011) were carved out of the supervised classification. These were overlaid with Landsat (TM) 1986 placed on (TM) 1991. This was further placed on (ETM+) 2002 and 2011. These satellite imageries were overlaid to determine the trends of the urban sprawl in Akure according to
This was tested on the Akure MUS where a place was identified through this tool and the information about it was shown in
This study developed a geospatial database for monitoring urban sprawl in Akure. The Database was tagged Database for Monitoring Urban Sprawl (DBMUS). The features of each layer were stored in separate table as feature class which were later combined together to form a comprehensive Database. The feature classes are Local Government, Wards, Road network, Water bodies, Settlement etc. All these were linked/joined to the base table to form the Monitoring Urban Sprawl (MUS) Database. The table were populated with the data acquired from the field (geometric and attribute) after taken care of necessary formatting and editing. The entities identified were used to form a relational database in ArcMap GIS. Both the attribute and spatial data were linked together to generate queries that can solve spatial problems. The database in this work was developed in ArcMap GIS which conformed to findings by [
Geo-information based assessment of the impact of Urban Sprawl in Akure, Southwestern Nigeria using Remote Sensing and Geographic Information Systems (GIS) Techniques had been carried out. Considering the dynamic nature of Urban Sprawl, the use of satellite imageries and Global Positioning System (GPS) has shown the great potentials that the new tools have.
Though, urban sprawl and expansion is something unavoidable. The emergence of haphazard development has shown the unplanned nature of the sprawl area and therefore, the need to manage the rapid expansion and growth in the town and the settlements around it require pragmatic approach to urban planning and management using modern technology. The study had generated geo-spatial information which can be used to direct urban development ahead of urban pressures, to the appropriate area. The databases generated both spatial and non spatial information that will be useful in managing the rapid growth and expansion through integration with Geographic Information System. To this end, establishment of Remote Sensing and GIS Centers in the State and Local Governments need to be encouraged. The database or Geospatial information like rate of expansion, facilities, utilities and services, percentage change in built-up areas are stored. The information in the database can be retrieved and updated at any time needed. This modern technology feature of GIS makes it suitable for monitoring urban sprawl and growth in Nigerian growing cities.
We appreciate the support of the Institute of Ecology and Environment Studies, Obafemi Awolowo University for the platform to carry out this research.
Usman, V.A., Makinde, E.O. and Salami, A.T. (2018) Geospatial Assessment of the Impact of Urban Sprawl in Akure, Southwestern Nigeria. Journal of Geoscience and Environment Protection, 6, 123-133. https://doi.org/10.4236/gep.2018.64008