The study examined the growth pattern of settlements in Oke-Ogun area of Oyo State, Nigeria between 1984 and 2011; and predicted the future growth pattern of settlements in the study area. Both primary and secondary data were used for this study. Primary sources of data include Global Positioning System (GPS), Landsat TM and ETM+ imageries of 1984, 1990, 2000, and 2011. Secondary data included administrative map and population data of the study area. Descriptive statistics and geospatial technique were used to analyse the data collected. The results showed a random pattern of settlement distribution in the study area. Results revealed that settlements covered about 0.52% of the total land area in 1984; 1.32% in 2000; and 3.78% in 2011. Whereas linear pattern of growth characterised the periods between 1984 and 1990; clustering, infilling, and fringes were the patterns of growth that characterised the periods between 1990 and 2011. The study predicted that, at an average 1.2% of annual growth rate, settlements will occupy about 44.37% of the total land area by 2031. The study concluded that settlements in the study area varied in the patterns of distribution; the area was dominated by indigenous settlements type with overconcentration of social and economic infrastructures in few centres.
Settlements are known to change spatially with time, worldwide but the patterns of such changes vary and factors that encourage spatial change are diverse [
Settlements in Africa are known to grow towards directional or multi-directional patterns, and can therefore be distributed linearly or in clusters [
Whilst studies exist on the specific influence of the different factors of urban growth in more developed countries, existing concepts on the growth pattern of most settlements in Africa have neglected the influence of the African culture, probably because studies on settlement growth that are based on the African traditional regions are scarce. Most studies that have adopted the [
The processes of settlement growth pattern in many developing and populous countries, including Nigeria are characteristically different from those of the planned developed countries. For instance, [
In most of the developing countries, urbanisation is largely unplanned. Thus, information of the growth patterns is required to develop adequate plan and strategies for future settlement growth. Nigeria, for instance, is made up of different cultural groups and this study exemplifies the growth pattern of a traditional region in the southwest Nigeria. The objectives of this study are to 1) assess the growth pattern and direction in Oke-Ogun region in Nigeria, which typifies a Yoruba settlement region, 2) account for the forces of settlement growth pattern, and 3) predict the future settlement growth trends. The main hypothesis is that settlement growth in the traditional African region is not well accounted for by the existing concepts on settlement growth.
The study was conducted in Oke-Ogun area of Oyo State, in the northern part of South-western Nigeria. The area is located between latitudes 8˚9'52.25"N and 8˚53'42.785"N, and longitudes 2˚42'50.205"E and 3˚48'36.008"E (
The study area experiences tropical equatorial climatic condition characterised by high rainfall and high temperature. While the annual rainfall is about 102 mm [
Primary and secondary data were sourced for this study. Primary data were obtained using Global Positioning System (GPS); Landsat TM and ETM+ imageries of year 1984, 1990, 2000, and 2011 of the study area; and ground-thruting. Secondary data included administrative maps of the study area from the Town Planning Office and population data obtained from the records of the National Bureau of Statistics. A detail list of the data used in this study is shown in
The satellite imageries (Landsat TM and ETM+ imageries) were digitized to extract the portions required for
Material | Source | Year | Scale/Path & Row | Resolution | Relevance |
---|---|---|---|---|---|
Landsat TM & ETM+ | http://glcf.umiacs.umd.edu | 1984-2011 | Path 191, Row 54. For all | 30 m | Classification, landscape pattern, change process, and CA with Markov |
Administrative map | LG. Town Planning Office | 1999 | 1:100,000 | - | Base map of the study area |
Population data | National Bureau of Statistics/NPC | 1991 | - | - | To derive population density |
GPS | Field survey | 2011 | - | - | Coordinates of settlements and infrastructures |
the study. Classification of the selected settlements was carried out through image enhancement, contrast stret- ching, and false colour composition. CA_MARKOV, a combination of Cellular Automata and Markov Chain/ Multi-Criteria/Multi-Objective Land Allocation (MOLA) land cover prediction procedures, was used to develop a spatially explicit contiguity-weighting factor. Based on this, the Markov transition probability matrix was computed and fed into Cellular Automata. The results are presented in map to show the trend of future growth and pattern of settlements in the study area. In addition, spatial metrics of the study area were computed both at the landscape level and at the class of land use level. At the landscape level, NP was computed to measure the extent of subdivision or fragmentation of the patch type.
Furthermore, a test of spatial disparity in the distribution of settlements in the selected LGAs of Oke-Ogun was carried out using Nearest Neighbour Statistical Analysis. In addition, the rate at which the settlements grew over the years was calculated using a settlement expansion formula represented as:
where: r = Growth rate;
ΔA = Change in area extent between 1984 and 2011;
n = Number of years (interval between 1984 and 2011);
Aο = Area extent of the base year (1984) [
Category | 1984 | 1990 | 2000 | 2011 | ||||
---|---|---|---|---|---|---|---|---|
Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Farmland | 2288 | 21.98 | 3501 | 33.63 | 3320 | 31.89 | 3707 | 35.61 |
Vegetation | 5814 | 55.85 | 4578 | 43.98 | 4719 | 45.33 | 3849 | 36.97 |
Grassland | 2233 | 21.45 | 2255 | 21.66 | 2179 | 20.93 | 2401 | 23.06 |
Settlement | 54 | 0.52 | 51 | 0.49 | 137 | 1.32 | 394 | 3.78 |
Water body | 3 | 0.03 | 5 | 0.05 | 39 | 0.37 | 33 | 0.32 |
Rock | 18 | 0.17 | 18 | 0.17 | 17 | 0.16 | 26 | 0.25 |
Total | 10,410 | 100.00 | 10,410 | 100.00 | 10410 | 100.00 | 10,410 | 100.00 |
Source: Derived from
Water and rock surfaces exhibited a small pattern of change which is less than 0.05% (
Planners and policy makers are usually bothered about the negative effects of landscape fragmentation and heterogeneous development. Reference [
The transformation of land uses between 1984 and 2011 reduced the proximity (ENNMN) of the neighbouring land use patches. The decreased in the ENNMN can be attributed to the expansion of the built-up areas and encroachment of vegetation into farmland areas. The AWMPFD increased slightly from 1984 to 2000 and declined in 2011 indicating the degree at which the shapes of the patches became more complex in later years. This may be due to road network expansion towards the rural areas in 1984 and 1990 as confirmed by the dissection change process. In effect of a growing dispersion and fragmentation of the landscape in the study area, there was a decreasing trend in CONTAG.
Expansion of settlements plays a key role in increasing the heterogeneity of the landscape. A temporal reduction of the CONTAG and increase in the values of PD, SHDI, and ED indicate a higher degree of land fragmentation and increasing landscape heterogeneity in the study area. The ED of farmland remained higher than that of other land uses. However, this dominancy could cause higher ED values. A noticeable change is observed in the ED of vegetation and grassland. The ED and PD of settlements also correlated each other in 2000 and 2011 because increase in PD leads to increase in ED as a result of new edge segmentation formed. The trend of unordered individual housing development especially in the fringe areas in the corresponding decades enhanced the fragmentation and the heterogeneous landscape development.
At the class level, NP was the most valuable because it was the basis for computing other more interpretable metrics (
Year | NP | PD | ED | ENNMN | CONTAG | AWMPFD | FRACTAL | COHESION | SHDI |
---|---|---|---|---|---|---|---|---|---|
1984 | 55 | 0.0053 | 1.6662 | 4264.1781 | 67.1895 | 1.1189 | 1.0452 | 99.4669 | 1.0288 |
1990 | 109 | 0.0105 | 1.8210 | 3307.3951 | 64.8643 | 1.1175 | 1.035 | 99.4076 | 1.1001 |
2000 | 134 | 0.0129 | 2.3453 | 2990.4438 | 62.8908 | 1.1392 | 1.0372 | 99.4924 | 1.1383 |
2011 | 219 | 0.02 | 2.4656 | 2338.2015 | 60.0873 | 1.1331 | 1.0334 | 99.3444 | 1.2308 |
Source: Computed from landsat imageries of the study area: 1984, 1990, 2000, and 2011. PD = Patch density; ED = Edge density; NP = Number of patches; ENNMN = Euclidean Nearest Neighbour Mean Distance; CONTAG = Contagion; AWMPFD = Area Weighted Mean Patch Fractal Dimension; FRACTAL = Fractal Dimension; SHDI = Shannon’s Diversity Index.
NP | PD | |||||||
---|---|---|---|---|---|---|---|---|
LULC | 1984 | 1990 | 2000 | 2011 | 1984 | 1990 | 2000 | 2011 |
Farmland | 5 | 13 | 4 | 7 | 0.0005 | 0.0012 | 0.0004 | 0.0007 |
Vegetation | 7 | 4 | 5 | 13 | 0.0007 | 0.0004 | 0.0005 | 0.0012 |
Grassland | 5 | 9 | 4 | 8 | 0.0005 | 0.0009 | 0.0004 | 0.0008 |
Settlement | 25 | 57 | 99 | 164 | 0.0024 | 0.0055 | 0.0095 | 0.0158 |
Water | 9 | 9 | 10 | 15 | 0.0009 | 0.0009 | 0.0010 | 0.0012 |
Rock | 4 | 17 | 12 | 12 | 0.0004 | 0.0015 | 0.0012 | 0.0014 |
ED | ENNMN | |||||||
Farmland | 0.8059 | 1.0544 | 1.8638 | 1.8219 | 6052.5776 | 2767.579 | 670.0000 | 4078.8309 |
Vegetation | 1.3572 | 1.1934 | 0.8767 | 0.8352 | 2909.0526 | 4202.287 | 7953.122 | 2624.8226 |
Grassland | 0.9201 | 1.0463 | 1.2240 | 0.9980 | 8816.9871 | 8399.939 | 3778.573 | 6308.1569 |
Settlement | 0.1606 | 0.2420 | 0.5310 | 1.0872 | 4103.4490 | 2814.812 | 2378.151 | 1827.8245 |
Water | 0.0695 | 0.0657 | 0.0695 | 0.0895 | 3158.9908 | 2859.566 | 5739.584 | 4434.3031 |
Rock | 0.0190 | 0.0402 | 0.1255 | 0.0994 | 2732.9349 | 2562.445 | 3884.762 | 2720.7175 |
CONTAG | FRACTAL | |||||||
Farmland | 97.347 | 97.784 | 95.368 | 95.933 | 1.0289 | 1.0295 | 1.0770 | 1.0758 |
Vegetation | 97.954 | 97.784 | 98.389 | 98.118 | 1.0758 | 1.0949 | 1.0918 | 1.0241 |
Grassland | 96.963 | 96.336 | 95.673 | 96.936 | 1.0797 | 1.0802 | 1.0927 | 1.0826 |
Settlement | 77.412 | 61.592 | 68.063 | 77.140 | 1.0303 | 1.0288 | 1.0286 | 1.0306 |
Water | 44.444 | 38.750 | 75.077 | 77.231 | 1.0578 | 1.0175 | 1.0445 | 1.0188 |
Rock | 72.297 | 74.324 | 69.145 | 74.825 | 1.0472 | 1.0459 | 1.0488 | 1.0418 |
AWMPFD | COHESION | |||||||
Farmland | 1.1443 | 1.1323 | 1.0948 | 1.1729 | 99.6225 | 99.6656 | 99.7324 | 99.6573 |
Vegetation | 1.1139 | 1.1240 | 1.1970 | 1.1096 | 99.6080 | 99.6363 | 99.5775 | 99.5925 |
Grassland | 1.1083 | 1.0829 | 1.1512 | 1.1129 | 99.1135 | 98.422 | 99.2992 | 99.1857 |
Settlement | 1.0477 | 1.0699 | 1.0779 | 1.1170 | 82.6079 | 77.0707 | 82.7432 | 92.7132 |
Water | 1.0865 | 1.0515 | 1.1584 | 1.1420 | 65.2764 | 55.3532 | 93.1108 | 92.5135 |
Rock | 1.0606 | 1.0459 | 1.0581 | 1.0556 | 79.3741 | 78.2587 | 76.4372 | 80.6286 |
Source: Computed from landsat imageries of the study area: 1984, 1990, 2000, and 2011.
one another. However, at the wake of 2000-2011 study period, the proximity of the farmland reduced to less than 1000 metres but rose again before the close of the phase. This is an evidence of low unordered level of socio-economic development in the study area. The ENNMN of settlement areas decreased of from 4000 meters in 1984 to less than 2000 meters in 2011. This indicates that the growth process was confined mostly in the margins of the existing built-up areas thereby leading to lower degree of isolation.
The CONTAG of farmland, vegetation and grassland are maximally aggregated, this conclusion was based on their low edge density (when a single class occupies a very large percentage of the landscape) that leads to high contagion value: a higher value very close to 100%, the implication is that these classes of land use are not highly fragmented. Settlement, water body and rock are averagely aggregated with values above 50%, especially the settlement as a focus of this study; this implies that the area is spatially distributed, though in terms of locations of each settlement, they are aggregated.
Fractal Dimension Index (FRACTAL) ranged between 1 and 2 reflecting a shape complexity across a range of patch sizes. The general FRACTAL value of all the classes of land uses was greater than 1. However, individual land use produced different values: whereas each of the farmland, vegetation and grassland yielded FRACTAL values of greater than 1, the built-up area produced a value of less than 1. However, rehabilitation and expansion of roads into villages in the 2000-2011 growth phase significantly increased the shape complexity of the built-up areas resulting in a highly convoluted, plane-filing shape perimeter.
Patch Cohesion Index (COHESION) measures the physical connectedness of the corresponding patch type. The Cohesion Index indicated that the physical connectedness of the general land use decreased between 1984 and 1990, but rose between 1990 and 2000. The index decreased again between 2000 and 2011. However, the ED and CONTAG values showed that the farmland, vegetation and grassland areas were maximally aggregated. Since, the higher the degree of aggregation, the higher the degree of physical connectedness, and therefore such land uses with higher degree of aggregation were highly connected. The degree of physical connectedness of the built-up areas gradually increased over time, indicating the merging of the previously segregated parts of the built-up areas such as the city core and fringe areas. The ED, CONTAG, and COHESION are strongly interdependent.
Resulting from the spatial metrics analysis, the degree of spatial concentration and dispersion of settlements in the study area yielded a value of 1 (SHDI = 1). This confirms that the growth pattern of settlements in the study area was spatially random.
The rate of growth and change processes were computed using urban expansion formula adopted by [
r = 1.2% (annual growth rate);
where: r = Growth rate.
ΔA = Change in area extent between 1984 and 2011;
n = Number of years (interval between 1984 & 2011);
Aο = Area extent of the base year (1984).
The result of the computed rate of spatial growth and change process was 1.2% annually. Based on this result, the change processes in the study area were found to be associated with dissection, creation, and attrition. However, there were some areas that did not change. In the 1984-1990 growth phase, the number of patches of homogenous area representing the built-up areas increased while the area coverage decreased. The built-up area grew along the road networks through a dissection process. Reference [
In the 1990-2000 and 2000-2011 phases, the growth process was mainly by creation (
Owing to the division of the area into three Local Government Areas, during the years 1990-2000, the settlement areas began to coalesce and getting scaled up. In addition, other new isolated settlements around the existing settlements became noticeable because several non-developed pixels some distance from an existing developed area are being developed through infilling process. This class of growth was characterised by new houses and similar construction surrounded by little or no developed land. During this period, 0.76% of farmland was built-up into settlements, with the growth following the axial corridors created by the road networks and existing built-up peripheries, a linear and fringe pattern could be observed here.
The land use transition continued in 2000-2011 with a different phenomenon of land conversion as compared
to the previous phase. Some parts of the built-up area was lost to farmland (2.06%), vegetation (0.23%), and 0.66% to grassland. This is a very rare case in settlement transformations. The cause was attributed to a monarchy crisis that ensued in 2002 in some parts of the study area, which resulted into house burning and general destruction of properties. In effect, many people fled for their lives in exile for years. The physiographic milieu created by this trauma is still obvious in the up till the time of this research. In the 2000-2011 growth phase, about one-third of the farmland was transformed to grassland, while 2.87% was converted to vegetation. Also, the vegetation land-cover lost 4.42% of its area to farmland, 0.94% transited to grassland and 0.01% to settlement. In Saki West LGA which lies towards the northern part of the study, settlements were more aggregated, and the built-up area increased and concentrated resulting in clustering pattern of growth. In Saki East and Atsibo LGAs, settlements were less aggregated, thus the degree of physical connectedness was very low. Infilling, linear, and fringe were the observed patterns of growth in these parts of the study area. On the whole,
The results of the Nearest Neighbour Analysis (Rn) based on LGA level reveals that Rn in Saki West LGA and Saki East LGA is 1.13, while the Rn for ATISBO LGA is 1.52. Going by these results, the indication is that spatial distribution of settlements in Saki West and East LGAs is random, but moving towards perfectly random. In the case of ATISBO LGA, the spatial distribution pattern of settlements is perfectly random (
On a general note, Random Settlement pattern distributions is simply an indication or characteristic of an indigenous settlements with low centrality rank, low access to services and facilities, low population growth, and high poverty incidence [
Based on annual growth rate of 1.2%, the expected spatial changes in the land use/cover in the study area in the next twenty years are shown in
LGA | No. of settlements* | Area (km2)** | Mean Rn (km) | Rn | Pattern |
---|---|---|---|---|---|
Saki West | 50 | 2318.149317 | 3.87 | 1.13 | Random |
Saki East | 33 | 1876.327256 | 4.27 | 1.13 | Random |
ATISBO | 26 | 1701.466591 | 5.68 | 1.52 | Perfectly random |
Sources: *Calculated from administrative maps of the study area, 2010; and **reference [
Land uses | Land uses in 2011 | Expected land uses by 2031 | Expected change in 2031 (km2) | % Change |
---|---|---|---|---|
Farmland | 3707.0162 | 4337.2718000 | 630.2556 | 14.53 |
Vegetation | 3848.64415 | 3028.9527500 | −819.6914 | −27.06 |
Grassland | 2400.604975 | 2279.4019750 | −121.203 | −5.32 |
Settlement | 394.021975 | 708.2519750 | 314.23 | 44.37 |
Water body | 32.7697 | 25.9239750 | −6.845725 | − 26.41 |
Rock | 25.81175 | 29.0662750 | 3.254525 | 11.20 |
Total | 10408.86875 | 10408.86875 |
Source: Computed from landsat imageries of the study area: 1984, 1990, 2000, and 2011.
an area of 708.25 km2, representing 44.37% change.
Cellular Automata (CA) was used to explain the spatial distribution of occurrences within each land use category. CA_Markov uses the output from the Markov Chain Analysis to apply a contiguity filter to “grow out” land use. The result revealed that the expected spatial changes of the settlements will be more visible at the fringes, probably, following the road network. While some of the small settlements will pass through scaling-up processes of growth, some others will be growing through infilling and outlaying processes. The expected spatial growth pattern of settlements in the next twenty years is presented in
In this study, spatial and temporal growth patterns of settlements have been investigated in Oke-Ogun Area of Oyo State, Nigeria, using descriptive statistics, remote sensing and spatial metrics techniques. The study revealed that pattern of settlements in the study area is random; while the pattern of settlements in ATISBO LGA was perfectly random, it was just random in Saki West and East LGAs.
Spatial pattern of changes was determined using land use/cover of the area in four different epochs. The result showed dynamics of spatial pattern changes among various classes of land use/cover with their transition processes. The growth pattern of settlements was discovered to be in various directions following the trend of the road network in many parts of the study area. The general landscape pattern (both at the landscape level and at the class level) of the areas was determined using spatial metrics; the result showed an indication of complexity with high level of heterogeneity specifically the settlements.
As the landscape keeps transforming, getting more complex, fragmented and building more patches especially the settlements, there is a process underway. Three major growth processes were found to be in progress in the study area, these are attrition, dissection and creation. In addition, the annual growth rate of the built-up area was found to be 1.2%.
The future growth pattern of settlements in the study area was projected to the next 20 years using CA_ Markov. The expected growth pattern for the next 20 years showed that infilling, outlying, and isolated kind of growth pattern will be experienced both at the core and fringe of the settlements.
Settlements appear to be the most dynamic human element over the earth’s surface. It needs serious geographical investigations and scientific explorations. This study, which examined the pattern of settlements’ growth in Oke-Ogun area of Oyo State, Nigeria, found the settlements in the area to be expanding on their fringes; therefore, there is the need to give physical development laudable priorities. The study revealed that there was spatial disparity in the pattern of settlements’ growth. Also, there was over concentration of central place functions in few places. The dominant growth process of settlements in the study area was attributed to creation process where both the settlement and area patches were increasing simultaneously. The result of landscape analysis showed that the area was becoming more heterogeneous and the trend of growth was toward the road network. Hence, the growth of settlements is more obvious at the fringe area. If this trend continues unabated, it may result in the expansion of urban centres into rural areas. In effect of this, the agricultural lands would be converted into other uses which in turn may translate into food scarcity in the area and, consequently affecting the national economy.
In addition, there was lop-sidedness in the location of infrastructural facilities; they were concentrated in a few favoured centres. This has culminated into upsurge in the population of the so favoured centres. The over- concentration of socio-economic services in few centres is an attracting force to rural populace to the centres in search of better economic activities; educational advancement; employment opportunity and better living condition. Again, the end result would be neglect of agriculture when able bodied individuals have fled the rural areas to have a taste of better living condition it is provided.
In view of the observed imbalances, it is desirable that remote sensing capabilities should be harnessed in spatial analysis in Nigeria to enhance reliable data. This will enable planners and decision makers arrest errors before they occur, as well as manage changes in the dynamic environment. This study, in the same way as noted by [