Computational and spatial approaches to the Nyanga
archaeological complex
Tendai T. Musindo
Dissertation submitted in partial fulfilment of the requirements for the degree of MSc
GIS and Spatial Analysis in Archaeology of the University of London in 2010
Institute of Archaeology, UCL
Picture (a) Nyanga terraces (picture adopted from Tempelhoff 2008)
Picture (b) Nyangui DEM and
terraces
Note: This dissertation is an unrevised examination copy for consultation only and it
should be quoted or cited without the permission of the Director of the Institute
Table of Contents
Table of Illustrations .......................................................................................................... iv
Acknowledgements ............................................................................................................. v
Abstract .............................................................................................................................. vi
CHAPTER 1: INTRODUCTION ....................................................................................... 7
1.1: Introduction .............................................................................................................. 7
1.2: Research Setting ...................................................................................................... 9
1.2.1 Location ............................................................................................................. 9
1.2.2 Climates ........................................................................................................... 10
1.2.3 Geology ............................................................................................................ 10
1.2.4 Agriculture ....................................................................................................... 12
1.2.5 Terracing .......................................................................................................... 13
1.3: Aims and Objectives .............................................................................................. 16
1.4 Research Problems and justification ....................................................................... 17
CHAPTER 2: ARCHAEOLOGICAL BACKGROUND AND THEORETICAL
CONSIDERATIONS ........................................................................................................ 19
2:1 Introduction ............................................................................................................. 19
2.2 Archaeological Background.................................................................................... 19
2.3 Theoretical Frameworks ......................................................................................... 22
2.3.1 GIS in archaeology .......................................................................................... 23
2.3.2 Man-Land Relationship ................................................................................... 24
CHAPTER THREE: METHODOLOGY ......................................................................... 26
3.1 Introduction ............................................................................................................. 26
3.2 Data Collection ....................................................................................................... 26
3.3 Manipulation of data ............................................................................................... 27
3.3.1 Remote Sensing ............................................................................................... 27
3.3.2 Geology Maps .................................................................................................. 30
3.4 Statistical analysis ................................................................................................... 32
CHAPTER FOUR: DATA ANALYSIS AND PRESENTATION OF RESULTS
(LARGE SCALE) ............................................................................................................. 33
4.1 Introduction ............................................................................................................. 33
4.2 Independent Variables ............................................................................................ 34
4.3 Dependant variables ................................................................................................ 37
4.4 Logistic Regression Analysis .................................................................................. 39
4.4.1 Univariate Logistic Regression Analysis ......................................................... 39
4.4.2 Multivariate Logistic Regression Analysis ...................................................... 41
4.4.3 Linear Regression ............................................................................................ 46
CHAPTER FIVE: ANALYSIS AT SMALL SCALE ...................................................... 51
5.1 Introduction ............................................................................................................. 51
5.2 Variables ................................................................................................................. 52
5.3 Univariate Regression Analysis .............................................................................. 54
5.4 Multivariate regression ........................................................................................... 56
CHAPTER SIX: DISCUSSION AND CONCLUSIONS................................................. 57
ii
REFERENCES ................................................................................................................. 59
APPENDIX ....................................................................................................................... 62
Appendix 1. Part of geology attribute table .................................................................. 62
Appendix 2 . Attribute table for grids used for the linear regression ........................... 63
Appendix 3 Commands for statistical analysis ............................................................. 68
iii
Table of Illustrations
Figure 1. Location of study area, view from Google Earth ................................................ 9
Figure 2. Picture showing the rugged terrain of Nyanga landscape ................................. 10
Figure 3. Distribution of terraces in Nyanga .................................................................... 11
Figure 4. Forestry- a common feature in Nyanga ............................................................ 13
Figure 5 Nyanga terraces (Adopted from Tempelhoff 2008) ........................................... 15
Figure 6 Digital Elevation Model from ASTER GDEM .................................................. 29
Figure 7 Geology reclassified ........................................................................................... 31
Figure 8 Distance measured from the dolerite boundary .................................................. 36
Figure 9 Map showing the terraces and randomly created non terraced areas ................. 38
Figure 10. The map shows distribution of terraces on the relative probability surface. ... 43
Figure 11. Map showing large areas of „yes‟ at 0.3 probability ....................................... 45
Figure 12. Probability surface at 0.6 probability .............................................................. 46
Figure 13. Nyangui Block G ............................................................................................. 51
Figure 14. Nyangui DEMs ................................................................................................ 52
Figure 15. Nyangui terraces and sites ............................................................................... 53
iv
Acknowledgements
I owe a debt of gratitude to a number of people who helped make this research a success.
Firstly, I would like to thank my supervisor, Dr. A. Bevan for his guidance and very
useful comments and suggestions throughout the writing of this thesis. I also want to
thank Dr. M. Lake for his mentorship and guidance through this academic journey.
Enrico Crema was always available for consultations, thank you. I am also greatly
indebted to Dr Robert Soper, who was always available for consultation. I am especially
grateful for the time he availed for a fruitful discussion we had on my research area.
When I was a Curator at Great Zimbabwe Monument, I got to know Professor Heinz
Ruther from the University of Cape Town when he was doing a heritage digitization
programme. The conversations that I had with him made my interest in GIS and Spatial
analysis in Archaeology grow.
I also appreciate support I got from Mr M. Chifamba from the University of Zimbabwe,
Archaeology Unit who helped me acquire most of the maps I used in this research. I have
also benefited from numerous discussions that I have had with him on the state of
archeology in Zimbabwe. At the National Museums and Monuments of Zimbabwe, I
would want to express my gratitude to the Executive Director Dr. G. Mahachi and
Regional Director of the Southern Region, Mr. C. Chauke for allowing me to take a one
year study leave to pursue my studies.
It would be a gross omission not to mention the support I received from friends and
relatives. Particularly I would want to mention my husband, Joseph Mujere who was a
pillar of strength throughout the programme. I would also like to thank Joseph
Chikumbirike who besides encouraging me has always been a good friend. Thanks are
also due to Dr. M. Manyanga who encouraged me to pursue post-graduate studies in
archaeology and also taught me to also seek to stretch the limits of my abilities. My
colleagues in the GIS and Spatial Analysis in Archaeology class were always supportive,
Thanks Niki, Giorgia, Kristin, Shayner and Eva.
Finally, I would like to thank the Cannon Collins Trust for funding my studies and my
travel.
v
Abstract
The study is situated in one of the eastern districts of Zimbabwe, Nyanga and looks at the
relationship between ancient terraces and different environmental parameters.
Particularly the parameters considered in the study are geology, elevation, aspect and
slope. Besides the physical environment, the study also looks at the relationship between
the terraces and the settlement sites. GIS software have been used to assemble data and
statistical packages used in the analysis. The logistic regression analysis has shown that
there is a correlation between the Nyanga terraces and the other variables which may
suggest that these environmental variables may have been considered in the location of
the terraces though it is noted that correlation is not equal to causation.
vi
CHAPTER 1: INTRODUCTION
1.1: Introduction
This research seeks to adopt GIS (Geographic Information Systems) to re-examine the
Nyanga archaeological complex in eastern Zimbabwe. The archaeology of Nyanga indicates
that the area has a long history of occupation with evidence of Stone Age material in the area
(Summers 1958, Soper 2002) and the history span into the present. Be that as it may, the
major focus of this study is the archaeological complex which has been dated from the 15th
century to the 18th centuries (Soper 2006). The cultural remains include a wide range of
features which include stone terraces covering escarpments, hills and valleys, pit structures,
enclosures and smelting furnaces. Besides being extensive, these archaeological features are
also quite spectacular. Particular attention is given to the terraces which Chirawu (1999)
regards as the most impressive component of this complex covering an area of „at least‟
5000km². These terraces are not a unique phenomenon to the Nyanga landscape only, but as
argued by Sandor writing on the ancient terraces of Mexico, terraces are widespread in both
time and space among many cultures across the world (Sandor 1987). Chirikure and Rehren
(2004) admit that the Nyanga agricultural complex constitutes probably the largest human
modification of the landscape in southern African prehistory (Plug et al 1997, Soper, 1997,
Summers 1958).
The terraces of Nyanga have generally been accepted within the wider academic circles and
the community at large that they saved as mechanisms of soil management for agricultural
purposes. However, there are a handful of scholars like Kritzinger (2007) who have argued
that these terraces where used for mining purposes and not for agriculture. This is of course
contrary to the widely accepted argument that the terraces were used for agricultural
purposes. The claim has been dismissed by several scholars among them Love and Walsh
(2009) who argues that there is no evidence to support the mining activity claim. This „new‟
debate is not part of the discussion in this study which adopts the general consensus by most
researchers who are in agreement that the Nyanga archaeological complex represents an
agricultural system. The major focus of the study is how the terraces relate to the surrounding
environment. The study analyse these archaeological terraces against topography, geology,
soils, climate, vegetation, and other archaeological remains found in the area particularly the
pit structures which are generally associated or interpreted as settlement sites (Summer, 1958,
Chirawu 1998, Soper 2002).
7
Although the major focus is the ancient terracing, the study also notes that the practice of
terracing in the area has not been entirely abandoned but is still common among the locals. It
is within this context that when the research examines the factors that were put into
considerations in the location of these terraces it will also consider the indigenous knowledge
systems in agricultural practice.
Besides looking at the relationships between the terraces and environmental factors which
aids to the interpretation of location of these terraces, the study can be of much benefit in
predictive modelling, a concept that is widely used within the context of heritage
management particularly in archaeological impact assessment. The study establishes a
predictive surface which predicts where terraces are most likely to be found in the region.
This is particularly helpful in locating terraces in those areas yet to be archaeologically
surveyed.
The study makes both use of open source software, GRASS and commercial software ESRI
ARCMAP. These are used alternatively where choice is determined by the nature of task
performed. This choice of which software was used where is discussed in detail in the
methodology chapter. Statistical packages particularly “R” software is also used in analysing
the data.
8
1.2: Research Setting
1.2.1 Location
The Nyanga area is situated in the north Eastern district of Zimbabwe in an area commonly
known as the Eastern Highlands due to the presence of a mountainous terrain. The Eastern
Highlands stretches from Nyanga area to further south into Chimanimani Mountains. Though
there is evidence of terracing further south (Soper 2002), the current study focuses only on
the north eastern area which is the Nyanga District The area is mountainous with the highest
mountain being Mt Inyangani which at 2595 m above sea level is the highest mountain in
Zimbabwe. Nyanga is therefore characterised by a hilly and rugged terrain and crucial to
note is that by 1500 AD, the inhabitants of Nyanga had already devised a soil management
mechanism of overcoming this rugged terrain in the form of terraces.
Figure 1. Location of study area, view from Google Earth
9
Figure 2. Picture showing the rugged terrain of Nyanga landscape
1.2.2 Climates
Zimbabwe is divided into five agro-ecological zones with zone 1 being regarded as most
productive and region 5 being the least agricultural productive. Nyanga district is one of the
few areas with most parts of district categorised as falling into the agro-ecological natural
region 1, which makes 2% of the land of Zimbabwe. The region is basically characterised by
good agricultural soils and receives rainfall of above 1050mm which is quite high if
compared with annual rainfall in the Lowveld region (agro-ecological region 4 and 5) for
example which often has rainfall of below 400 mm per annum (Vincent and Thomas, 1960).
The region is characterised by marked seasonality with almost all rainfall occurring between
November and March (Soper 2002).
1.2.3 Geology
The available maps for geology of Nyanga do not cover the whole region. There are only two
maps of which one of them only covers a small part of the district. For the purposes of the
study the base map for the geology was the one that covers much of the Nyanga area, which
is described by Soper as the one which “covers Nyanga highlands north of Nyanga town,
10
extending west just across the Nyangombe river” (Soper, 2002). This geology map then acted
as a boundary representing the region of analysis.
The map below shows the distribution of terraces as indicated by Soper (2002) and shows
those which were taken for analysis.
Figure 3. Distribution of terraces in Nyanga
11
Chirawu (1999) gives a summary description of the geology of Nyanga area, which is
adequate for this study. The geology mainly comprises granitic rocks of older tornalites,
intermediate and adamellites. There are few occurrences sedimentary rocks mainly sandstone
and then there is also dolerite. This dolerite found in this geology area defined by the
southern geology map, according to Soper (2002) covers about 20 percent. Soils in the
Nyanga area correspond mainly with the geology, where there is a predominance of granite,
there is sand soil which is not very productive in as far as agriculture is concerned. Chirawu
(1999) also noted that the umkondo sediments give rise to fine grained soils which are
productive but highly acidic. On dolerite, soils have a high clay content which makes them
more fertile as compared to granite soils. Soper (2002) observed that terracing is common in
both the granite and the dolerites but particularly on dolerite geology. There are other
geology types like black magnetite which is the source of iron ore and iron working forms
part of the archaeological complex.
1.2.4 Agriculture
The region is characterised by specialized and diversified farming region with plantation
forestry, fruit and intensive livestock production. Plantation forestry is as a result of the rich
soils in steep slopes and it is a method of keeping the soils together. Coffee and tea are also
grown in the area on a commercial scale. Maize is generally grown but just enough for
subsistence. Tempelhoff (2008) notes that rainfall is considered the major limiting factor in
Southern Africa though he highlighted that there are other areas in the same region which
receives above average rainfall with a maximum of 1500mm. These rare areas include
Nyanga area, so in Nyanga, the problem is not about water, but the major challenge is the
hilly or rugged terrain which without proper soil management mechanisms cultivation
agriculture is very difficult.
12
Figure 4. Forestry- a common feature in Nyanga
However, there are patches of Nyanga which do not fall into this category and this includes
areas like Nyamaropa area which falls under agro-ecological region 2 receiving an annual
rainfall of 600 -800 mm (Vincent and Thomas, 1960). In these areas the vegetation is mainly
miombo woodland and terminaliaComretum in the lowlands of which both reflect
considerable human influence (Mandondo 1997).
1.2.5 Terracing
Terraces are not a unique phenomenon to Nyanga, rather they are found all over the world.
Greece, for example has recorded a number of terraces (Price and Nixon 2005). Another
example are the terraces located in the Maya Mountains, which according to (Healy et al
1983), are beginning to provide key insights into alternative techniques of intensive
agriculture employed by the Classic Maya (AD 250-900). Connolly and Bevan (upcoming)
note that terracing is actually done to manage geomorphic processes with the terrace wall
construction and sediment filling aimed at reducing field slope angle and length. Chirawu
(1999; 3) steep land soil management can only be achieved by means of terraces. The
practices that conserve water also conserve soil for high productivity, which is the real goal
for soil management. In the case of Nyanga terraces, Chirawu (1999) groups them into three
based mainly on how they were constructed. The first category is the ridge terraces with
13
channels which are usually wide and narrow with no depressions for impounding water.
There are the broad terraces, which are used to conduct excessive water, often used where
water conservation is of less importance and finally the bench terraces which are common on
very steep slopes, where walls are built along as a contour of a field and the area behind filled
with soil (Chirawu 1999).
Soper (2002) argues that even though there are other parts of Africa where there are the
terrace systems, there is nowhere else where they are as extensive as the one in Nyanga,
Zimbabwe. Admittedly, the Nyanga complex has been attributed to agriculture. Studies by
Soper (1985, 1996, 2002, 2007), Sutton (1983, 1985, 1962), Pikirayi (1993, 2000), Beach
(1996, 1995) and Chauke (1996) have shown that the Nyanga complex was used for
agriculture. Soper (1996) talk of the complex as being as a result of specialised farmers who
had been pushed aside by expanding zimbabwe culture groups. Although the functions of
terracing in different parts of the world tend to be different but the major ones are as
irrigation mechanism or as soil management mechanisms on slopy areas and the later is likely
the case for the Nyanga terraces. Summarised the functions of terracing for these major types
include creation of a stable topographic base for crops, soil retention and erosion control, soil
accumulation by sedimentation or hand filling, water control ranging from water spreading to
runoff management, irrigation, and ponding, and other microclimate effects. Two basic
elements in nearly all terracing are the retaining walls (risers) and interwoven fields (treads).
Walls vary from very low to several meters high, from single walls to complex series. Wall
construction materials range from in situ bedrock to stones, earth, and living vegetation or
other organic materials. Some terraces are built as permanent structures whereas in some
systems such as runoff agriculture, their design, placement, and use are more flexible.
Chirawu(1989) He gives a background of the different forms of terraces on the African
continent, with the notable terracing being found in South Africa, East Africa, southern
region of Ethiopia and in the Nuba, in Sudan. He also admits that the terraces have been used
differently in the different parts of the continent. In the case of Nyanga, noteworthy is that
terracing is still practiced by some subsistence farmers in the area.
14
Figure 5 Nyanga terraces (Adopted from Tempelhoff 2008)
15
1.3: Aims and Objectives
The study aims to investigate the relationship between terraces in the Nyanga area and
different environmental variables. In the end it seeks to establish factors that were considered
in the establishment of terraces in the area. The environmental factors are not considered in
isolation but they are considered in relation to terraces and other archaeological features in
the region and mostly pit structures which represent settlement sites and forms part of the
archaeological complex.
Firstly the research takes a „global‟ approach where the whole Nyanga area is investigated
and then smaller units are analysed for finer resolution data, and this include hydrological
modeling to determine the function of the terraces especially in the management of runoff
and minimisation of soil erosion. The objectives of the study can be summarised as:

use GIS to deduce logical relationships and interpretation of the Nyanga
archaeological complex

To do a statistical analysis so as to get an insight of factors that was considered in the
location of sites (settlement / terraces).

To use the statistical results to deduce any meaningful relationships and understand
the indigenous knowledge systems.

To understand the agricultural system
of Africa, not only the crops grown as
evidenced from study of the grains or crops (ecological) studies and the botanical and
bioarchaeology which has been done but also the field systems.

To produce a predictive surface of terraces. Assessing how far the environmental
variables could be used as predictors of terraces.
Null Hypothesis - There is no correlation between terraces and variables of geology, slope,
aspect, elevation and settlement sites.
16
1.4 Research Problems and justification
This research comes on a background where much has been done on the archaeology of
Nyanga from archaeological surveying to analysis of excavated material with much more
recent work on the archaeology of Nyanga being by Chirikure and Rehren (2004) who
focuses on iron production in Nyanga. Mitchell (2004) in a review of Soper (2002) argues
that Nyanga has attracted interest from earliest phases of Zimbabwean archaeology but it is
only recently that “attention has been paid to the wider landscape surrounding the few
excavated settlements or to integrating data from them with the evidence for past agricultural
practices embodied in that landscape's terraces, cultivation ridges and water-delivery
furrows”. Mitchell argues that the main focus in these studies has been placed on
investigating construction and use of agricultural terraces and also establishing the age of
settlements and emphasizing the recovery of plant and animal remains and the resultant has
been “illustrations of very high quality, which will stand as a landmark study for many years”
(Mitchell 2004). This suggests that in spite of much work done on the Nyanga archaeology,
there are still some scholars who argue that the research is inadequate and therefore advocate
for more archaeological investigation particularly on the terraces.
Sutton argues that “as much gained from the tools used in cultivation, so is the actual fields
themselves” (Sutton 1983; 26). He also observed that “fields in African archaeological
contexts are usually forgotten in archaeological and ethnographic accounts of agriculture,
despite most of the surface of Africa having evidence of having been cultivated. He argues
that general clues to former agricultural activity may be recognisable from crop marks or
vegetation pattern but specific marks remain conspicuous, but in the case of Nyanga, it is
evidence of fields is very spectacular in the form of terraces. This research therefore seeks to
fill in this gap proposed by Sutton by way of doing an analysis of terraces in their holistic
nature.
The project “Agricultural History and Archaeology in Nyanga and Adjacent Districts of
Zimbabwe” directed by Robert Soper and funded by The British Institute in Eastern Africa
yielded quite a comprehensive insight into the archaeology of Nyanga. As the title of the
project suggest, the project focused on the agricultural history which by implication meant
the terraces were one of the major focus. It is in this context that one ponders what this
research is to achieve after more than a decade of research in the area. This research sought to
17
use a „new‟ technology in the form of GIS in the analysis of terraces. The „new‟ technology is
expected to offer new insights into interpretations of occurrences of terraces. There has been
an analysis of the terraces, which established relationships between the terraces and other
cultural remains and environmental variables. Chirawu (1999) did an analysis of comparing
different features found in different geological zones, particularly on terraces he looks at the
different types of construction found within each geology type. These studies have left a gap
especially on proving statistically what the relationships suggest, of which the current study
seeks to fill this gap by way of doing statistical analysis of the relationships. Soper (1996)
noted that terracing favours the dolerite but also extends into neighbouring areas of granite
and sedimentary rocks. It is within this background that the current study seeks statistical
backing of the claims.
Besides using “new” technology into the analysis in the form of GIS software, one of the
major inspirations of this study comes from Sandor (1987) who echoes that “terracing
constitutes some of humanity‟s strongest and most enduring efforts to manage geormophic
processes in agriculture and to conserve land resources. The array of terracing strategies
among past and present agricultural societies reflects the high degree of indigenous
knowledge of soil and landscape processes”. In a way the research seeks to get into the
minds of the populations responsible for the terracing and see if they had any considerations
in the location of their agricultural fields.
18
CHAPTER 2: ARCHAEOLOGICAL BACKGROUND AND
THEORETICAL CONSIDERATIONS
2:1 Introduction
This chapter has two main sections with the first section outlining the archaeological work
carried out in the Nyanga district. The study acknowledges that much work has been done in
the area hence detailed history of archaeological investigation of Nyanga area is already
available in other works (Summers, 1958, Soper, 1996, 2002, 2006 and Chirawu, 1999). This
section therefore seeks to only review those works most relevant to the current study. Also
reviewed is more recent work which is not mentioned in neither of the works, which also
means a review of the later archaeological investigations of the region. The archaeological
background would then expose how the current study is situated within the broader contexts
of archaeological research undertaken in the area. The other section of the chapter discusses
the theoretical frameworks in which the study is based. The theoretical framework revolves
around the quantitative approach to archaeology and particularly the use of GIS in answering
archaeological questions. The discussion has also been downsized to regression analysis or
predictive modelling which forms the basis of the current analysis.
2.2 Archaeological Background
There has been a long history of archaeological work carried out in the Nyanga area as
compared to other parts of the country. The major researchers in the archaeology of Nyanga
are Summers, Sutton and Soper who have worked extensively in the area. Archaeologists‟
attraction to the Nyanga area owed much to the complex nature of the archaeological features
found in the region. The most significant of these archaeological features include terraces and
the stone walls, which compares with Great Zimbabwe, though admittedly there has not been
much work on the area of establishing the link between Great Zimbabwe and Nyanga. It
would not be surprising that most researchers who worked in the region had original plans of
establishing the link between Great Zimbabwe and the archaeological complex found in this
eastern part of the country. The history of archaeological investigation as indicated by
Chirawu (1999) can be traced to the early 19th century with archaeological findings recorded
in the early explorers diaries to more scientific exploration by researchers like Summers with
findings illustrated in his book „Nyanga‟ (Summers 1958).
19
Among the major achievements of the long history of research is the construction of the
culture history of the area and the identification of the builders of the archaeological complex
(Chirawu, 1999). Although, in terms of research coverage, Great Zimbabwe has received
more attention, there have been some similarities in terms of the nature of studies conducted
at Great Zimbabwe and on the Nyanga complex. Soper who worked on the Nyanga complex
admits that much work concentrated on structures and little attention was given to the use of
space or function of the structures (Soper 2006). Even though Soper acknowledges this
shortfall, his work seems not to address the issue as more attention is given to how the
different archaeological features of the Nyanga complex were utilised at the expense of
spatial analysis. At Great Zimbabwe, few scholars have attempted to look at the issue of use
of space. One such scholar is Huffman (1996) who uses ethnographic models to understand
use of space at Great Zimbabwe. Whereas Huffman uses ethnography to interpret functions
of different structures, Soper (2006, 2002, 1996) and Sutton (1984) mainly uses parallels
from East Africa to derive functions of the archaeological features of the Nyanga
archaeological complex. Both Soper and Sutton had vast experience in working with
irrigation schemes and terraces of east Africa like the Engaruka in Tanzania and Marakwet in
Kenya. It is therefore by no means a surprise that coming across similar structures in the
Nyanga they would argue that they were used for the same purposes as those that they found
in east Africa. As a result, most of the functions of the archaeological features of the Nyanga
archaeological complex have been interpreted using east-African models.
Another example of paralleling the Nyanga agricultural complex with East African is Chauke
(1996) who like Sutton draws parallels between paths in the Nyanga complex to the ones
found in the Konso system of Ethiopia where they were used to drive cattle for watering.
Chauke then concluded that the same could have been the case of Nyanga pathways. It is
however, noteworthy that there has been a consensus among archaeologists working in the
area that the archaeological complex represents an agricultural system. The current study
however focuses on terraces, seeking to establish relationships between the terraces and their
environmental parameters and also terraces and other archaeological features particularly the
settlement sites through use of GIS.
The interest in terraces can be traced to Sutton who compares the terraces of Nyanga and
those found elsewhere in the region especially in Tanzania. Besides comparing the Nyanga
20
terraces and those of Tanzania, where he noted that while the terraces found in Tanganyika
were meant for irrigation, the one in Nyanga were mainly for soil conservation purpose,
Sutton‟s work is also pivotal in that he highlighted the need to understand Nyanga terraces in
the context of how they relate to other features of the archaeological complex, an idea which
is also pursued by Chirawu (1999). Chirawu (1999) analyses the relationship between
terracing and living structures focusing on the lowland areas. Chirawu (1989) argues that the
terraces largely surround the settlement sites represented in the form of enclosures and pit
structures. He examines the relationship between the terraces and the settlement sites and
noted that these are not always adjacent to each other. Among Chirawu (1999)‟s contribution
to the archaeology of Nyanga is his analysis of the patterns of construction where he
concluded that the field systems were constructed before the enclosures and the passages
which lead to the terraces.
Sutton (1989) also calls for independence of research when investigating archaeological
features like fields. He noted that “there is no such thing as an ordinary field; nor are there
typical techniques of cultivation, either in Africa or in other continents” (Sutton 1989). Sutton
explains that the peculiarity of fields, gardens and methods of tillage all reflect “both the
evolution of local cultural traditions and the potential (and equally the limitations) of their
environments, in the first place the soils and climate”. This is a very crucial observation
leading to one of the research questions of this study, which is to determine the extent to
which different environmental variable predict the location of terraces and in the context how
could indigenous knowledge systems be understood from studying the relationships between
terraces and their environments.
One of the major successes of Chirawu (1999)‟s work is the classification of the settlement
structures.
On the relationships of archaeological features and environmental variables
Chirawu takes a descriptive approach where he describes features that are associated with
different variables for example when he analyses features and geology he noted that there are
differences and variations between features in dolerite and granite. In his sampled area, he
noticed that the granite (sandy areas) have more stone structures as compared to dolerite area.
He also observed that the dolerite areas are intensively terraced as compared to the granite
areas which are almost devoid of terraces. While Chirawu looks at differences and variations
in features found in different ecological and geological zones, this study focuses on one
particular feature, terraces and their correlation with other features using statistical methods.
21
These are analysed to establish the level of correlation between the terraces and the different
environmental parameters.
Soper (2002) was a culmination of a long extensive study of the Nyanga archaeological
complex which started in 1993 with funding from the BIEA and finished in 1999. This study
is summarised in „The Terrace builders of Nyanga‟ (Soper 2006), which becomes the latest
intensive literature on the archaeology of Nyanga. Soper‟s investigations concur with his
predecessors noting that the whole Nyanga complex represents an agricultural society of
industrious farmers and stock raisers. He argues that African history is in fact agricultural
history with the crops, methods of cultivation being central to patterns of settlement. The
question posed by Soper is how far settlement pattern was shaped by agricultural patterns or
types of the season. The study by Soper is considered first of a kind which gives attention to
the fields themselves not the crops. Soper (2002) forms the basis of the location of
archaeological features of the current work.
There have been other archaeological investigations by Chirikure and Rehren (2004).
Acknowledging that Nyanga offers one of the most intensive agricultural systems, their focus
is on iron production, which is one of processes characterising the Nyanga archaeological
complex. There has been other „recent‟ works by National Museums and Monuments of
Zimbabwe on the Nyanga complex though it has been more of heritage management work.
As part of the Nyanga archaeological complex, Ziwa, is on the tentative list waiting to be
listed on the World Heritage List. The work of Mupira (.......) is an example that looks at the
archaeological landscape from a heritage management perspective particularly the inclusion
of local communities in the management of Ziwa national monument.
2.3 Theoretical Frameworks
The current study takes a quantitative approach, notwithstanding that not all scholars in
archaeology agree on this approach. It is with no doubt that GIS is part of processual/new
archaeology thinking. The new archaeology is characterised by quantitative approach to solve
archaeological questions. This section discusses some of the criticism which faces such an
approach and then justifies why the approach has been taken.
22
2.3.1 GIS in archaeology
The debate surrounding application of GIS in archaeology cannot be separated from the
theoretical developments in archaeology in general. This quantitative approach has seen
much of its criticisms from post processualist who argues that the human cognitive aspects
are removed if quantitative methods are used. Scholars like Knapp and Ashmore (1999) argue
that archaeologists‟ focus is on what people did to the land and how it aided or constrained
them rather than what they thought or felt about it. They noted that central to post
processualist thinking is the active role of individuals in constructing and interpreting the
world around them (symbolic expression).
From its inception in archaeology, GIS has faced a number of criticisms with the extremists
viewing it as „evil‟ and therefore should be abandoned completely. One of the major
criticisms on use of GIS has been that it oversimplifies the world. GIS was initially used in
geography and its transference to archaeology is therefore likely to encounter problems
(Gaffney & Van Leusen, 1995). The criticisms of use of GIS have been divided into
epistemological
insufficiency,
ontologigal
inadequacy,
ethical
inconsistency
and
methodological insufficiency. Epistomelogical insufficiency is argued to be in that GIS
restricts what we can know from the past while ethical inconsistency is cited as a problem
especially in heritage management where it lacks community participation.
This study argues that GIS is a useful tool and only misuse can lead to the accusations. As
noted by Witcher (1999), GIS actually provides the landscape archaeologists with a useful
tool for contextualising and describing survey data. Witcher (1999) however, is of the
opinion that the danger is in confusing such descriptions with interpretation and he noted that
due to large amounts of environmental data involved, such confusion often promotes
excessive environmental determinism. However, he noted that GIS can move beyond
description and begin to provide an interpretive environment grounded explicitly within
developments in landscape theory. He also acknowledges that GIS is beginning to be
humanised and as such dismissing GIS as environmental deterministic will be arguably
misguided.
23
Following Connolly and Lake (2006)‟s argument, it should be noted that GIS operation are
not automatic as assumed by some scholars. Connolly and Lake (2006) argue that it is
essential that specialists responsible for digitising, processing and analysing data be closely
integrated with both project design and data collection. Thus, GIS will rarely contribute in
any meaningful way if just given to a GIS expert with no understanding of the original goals
of an archaeological project. GIS has its own advantages if used appropriately. Comparing
GIS and other available options like paper maps, Connolly and Lake (2006) noted that GIS
offers advantage of enabling temporal patterning unlike the former which offers static
interactions. The other advantage of using GIS is that it offers multidimensional depictions of
features. Three dimensional views are very difficult to render on paper. On the other hand,
paper maps are flat and the process of projection of a curved surface to a flat one often
produces distortions. With GIS, updating of maps is easy and it is also easy to relate to non
spatial data like attributes and all these advantages makes GIS a vital tool in analysing
archaeological data hence adopted in the current study.
Other debate emanating from the use of GIS is the debates as to whether GIS can be regarded
as a tool or science. However, there are other ongoing debates where scholars argue whether
GIS is a tool or science. Those arguing for GIS as a tool argue that it is a tool applied in
geosciences while those who view it as a science then see GIS as a method for developing
and testing spatial theories. From an archaeological perspective, the theory viewing GIS as a
tool is sufficient, however limiting. The tool aspect is correct but limiting.
2.3.2 Man-Land Relationship
The man-land relationship which is core of the current study has been questioned by other
schools of thought particularly theories such as modern post processual or interpretive
paradigm which argues that the human dimension cannot be understood not only through
man-land relationships but man and his cultural landscape and social practices. This school of
thought has culminated in phenomenological thinking where interpretations are supposed to
be subjective.
The man- land relationship has seen GIS being put on spotlight for being environmentally
deterministic, particularly the use of regression models for predictive modeling. The concept
of „environmental determinism‟ argues that human activities are greatly influenced by the
surrounding environment. In conducting a GIS analysis for archaeology, the other challenge
24
is the use of modern environment as a proxy for past environments. Admittedly, the approach
has its shortfalls and archaeologists using GIS have very limited alternatives. The current
study was caught in this trap where the variables were taken from the present day
environments. For example the study makes use of very recent quickbird imagery, the Aster
data which formed the Digital Elevation Model. This is therefore with the assumption that the
height above sea level and the geology remain unchanged over the years. Other recent data
used included the geology maps. There is always a problem in using modern data to interpret
archaeological findings. Chirawu (1999) highlights this problem in relation to the current
study area, where he noted that Nyanga area‟s modern classification of suitable agricultural
productive land is classified into seven classes with the first class being the most suitable, but
“heavy terracing is actually found in land class 6 and seven which is suitable only for rough
grazing or sometimes afforestation” (Chirawu, 1999). This is an example of illustrating how
using recent data to interpret archaeological phenomena can be misleading.
In spite of limitations posed by using GIS in archaeology, it should be noted that it can still
offer a lot in archaeology. Predictive modeling is very useful in the area of cultural resource
management, particularly in implementing Environmental Impact assessments where
archaeologists are always found with limited time and resources to do a complete survey. In
such cases, predictive modeling is a very useful tool, though it has its own limitations. It is
within this context that the study acknowledges that if predictive modeling is used in its
narrow sense, it exposes the study the criticism of being environmentally deterministic.
25
CHAPTER THREE: METHODOLOGY
3.1 Introduction
This chapter discusses the process through which data for the research was acquired and the
methods used in gathering the data. The manipulation involved data sorting using GIS
software. The methodology chapter also discusses the processing of some maps which were
used in the analysis. The study adopted a large scale where analysis was to be done for a
larger region of Nyanga and then at a smaller scale where one study area of approximately
7km by 5 km was chosen for further analysis. The smaller region or finer resolution analysis
was done for Nyangui block G. To decide on which region to do on a finer resolution some
background information on the region was sought. The decision to work on Nyangui Block G
was reached at because of the dense terracing found in this block which is found in
conjunction with other archaeological features, mainly the pit structures.
3.2 Data Collection
In the acquisition of the data, accuracy and precision was taken into consideration as results
of GIS analyses are only as good as the data used to produce them. Gathering information
also included data on different environmental parameters which were to be examined for
correlation with terraces. The environmental parameters considered were geology, elevation,
slope and aspect. The dolerite geology type was considered to provide fertile soils for
agriculture. The other data which formed part of the variables to be tested against terraces
were pit structures, which are generally believed to be settlement sites. The data gathering
process therefore sought information on the variables mentioned.
Data collection for this research mainly involved assembling of relevant maps for the Nyanga
area and information on sites particularly the terraced landscape. Two geology paper maps
were sourced from the Geological Survey of Zimbabwe, Harare, Zimbabwe. As mentioned
elsewhere, the geology maps available from the Geological Survey Department do not cover
the whole area, so for the purposes of this research the southern map which covers the bigger
part of Nyanga was used. These maps provided useful information on the geology of the area.
For the elevation data, quickbird imagery from Aster GDEM web page was sourced. The
26
other maps which were used in the study (slope and aspect) were derivatives from the Digital
Elevation Model (DEM).
The data collection process also yielded some aerial photographs, which later proved to be
very difficult to use in the absence of ground surface verification. It was felt that in the
absence of ground verification, the aerial photographs could not be used so as to avoid
situations where features could be misinterpreted. For example in the case of Nyanga aerial
photographs it was difficult to differentiate between modern day ridges and the ancient
terraces though in some areas they were very clear.
It should be acknowledged that to get primary data on the archaeological features like the
geographic position and detailed description was a challenge. Though this primary data in the
form of site record sheets is available at the National Museums and Monuments of
Zimbabwe, Harare, at the time of the research, it could not be accessed. As a result
information on the features was based on secondary data. Most of the data used in this
research is from archaeological work which has been conducted in the area. The relevant data
mainly comes from research by the British Institute in East Africa and the University of
Zimbabwe which was directed by Robert Soper. Given the detailed data in Soper‟s (2002) it
is without doubt that the area under investigation was extensively surveyed. Soper (2002)‟s
monograph formed the basis for much of the data on archaeological features used in the
research. However, it is important to highlight that total survey of an area, such as Nyanga is
almost impossible owed to many factors which range from resources in terms of both
finances, time and human resources to the terrain of the region.
3.3 Manipulation of data
This section looks at how the different data sets were integrated into a GIS system. As
mentioned earlier, ArcMap and GRASS GIS software was used alternatively, utilising the
strength of either software in executing certain functions.
3.3.1 Remote Sensing
Remote sensing defined simply as the “studying an object or a phenomenon from a distance”
(Wiseman and El-Baz, 2007) particularly the use of satellite images was adopted for some of
the data used in the research. This included the use of quickbird imagery from Aster web
page, which was used for acquiring elevation data. The Digital Elevation Model used in the
27
study was downloaded from the website; http://www.gdem.aster.ersdac.or.jp/search.jsp,
which is an ASTER GDEM (Global Digital Elevation Model) web page. The DEM available
on the web page is acquired by a satellite –borne sensor which is called ASTER hence
ASTER GDEM (http://www.gdem.aster.ersdac.or.jp/ ). Selection of tiles which covers the
region under investigation was done using rectangle bounding coordinates. Four tiles
covering the area of study were downloaded. These come as a zipped folder where the DEM
is in a tiff format and needs to be extracted from the zipped folder. The tiff file could be
loaded directly in ArcMap, but these were separate so they had to undergo a process of
combing the four separate tiles, which is easily done in ArcMap. In ArcMap, the tiff files
were converted to raster which was then combined using the mosaic function in the Data
Management Tools, under To Raster tool in ArcMap. The ASTER DEM had a resolution of
30m which means the cell size is 30X30m. The process produced a large DEM covering
some areas of which data was not available, so the DEM was reduced to fit the geology map
using the function change region settings in GRASS. The ASTER GDEM is not perfect and
as such for the DEM to match the digitised features, it was moved 500m along the y axis.
28
Figure 6 Digital Elevation Model from ASTER GDEM
29
Aerial photos
Although, the aerial photographs later proved difficult to use especially in the absence of on
the ground surface verification, a sample was processed. The processing indicated that in the
presence of necessary resources, it can be of immense contribution to the pool of data for
Nyanga archaeology. The aerial photographs available have a scale of 1:25 000, meaning
4cm =1km. Length of each photographs is 24cm which is 6km. The processing involved
establishing on the physical ground areas covered by the photographs so as to georeference
them. A total of 6 photographs were georeferenced using reference of Google earth.
3.3.2 Geology Maps
The geology maps were scanned and the scanned version was loaded into ArcMap as a .tiff
document. The tiff image was georeferenced which is defined as the establishing of the
location in terms of map projections or coordinate systems. The georeferencing was done in
ArcMap georeferencing tool. The study adopted the UTM coordinates which were available
on the map (UTM 36 South). Grids were created using GIMP, an open software for
photograph processing. The intersection of the grids acted as the control points for which
georeferencing was done. From the control points, the maximum residual was 43.2000 which
was quite marginal that the scanned image was rectified and georeferenced with an error of
about 40m.
Control Point
X
Y
Residual
1
490000
8000000
43.20407
2
490000
8030000
33.00001
3
450000
7990000
36.15600
4
450000
8030000
38.45777
The digitisation was done in ArcMap, which offers an easier function of digitising of maps.
The result was an ESRI shapefile which could be integrated into the GIS software. The
georeferenced map was then digitised where each geology zone was saved as a polygon and
this resulted in several polygons with each geology type standing on its own, representing 48
geology types. The geology types were further re grouped into dolerite and non dolerite and
the resultant map is shown below.
30
Figure 7 Geology reclassified
Digitising
On a larger scale, geology and terraces were the only features digitised. Contours, rivers and
pit structures were digitised for the finer resolution region of Nyangui Block G. In
ArcCatalogue, created shape file which was a polygon, polyline or point depending on what
is to be digitised.
31
Features
Type
Geology
Polygon
Contours
Polyline
Pit structures
Points
Rivers
Polyline
Terraces
Polyline
Polygon
3.4 Statistical analysis
The final part of the research was to conduct some statistical analysis with results presented
in the next chapter. Statistics in archaeology is used to fill in the gap between data and
opinions of researchers. For the statistical analysis, R, a free statistical software was adopted
and specifically a regression analysis was performed on the data. The data should have a
pattern and from the pattern comes the opinions. So with statistics one can see if the pattern
confirm the opinion or whether it suggest an opinion. The opinion in this case is that since
dolerites are fertile, we expect more of terraces in the dolerite and alluvium geology. The
preliminary analysis used the univariate regression analysis and then followed by further
investigation using multivariate regression analysis. A regression model is useful in
visualising relationships which can either be negative or positive. Positive when the
dependant variable increases when the independent variable also increases and positive if the
opposite.
The regression model will then be tested using Kvamme‟s Gain statistics. It has since been
noted that the ideal situation of testing the patterns suggested by statistics in archaeology
would be ideally by going into the field, but as a result of several factors ranging from
finances, time and feasibility of going into the field, archaeologists then has to use
alternatives to test the patterns suggested by statistical methods. In this research, a Kvamme‟s
Gain statistics was employed.
32
CHAPTER FOUR: DATA ANALYSIS AND PRESENTATION
OF RESULTS (LARGE SCALE)
4.1 Introduction
This chapter presents the process of data analysis and the results from the analysis at a large
scale. The large scale analysis is the one that looks at the relationship between terraces and
their environmental environment in the Nyanga region and since large area is involved the
analysis is referred to as at courser resolution. The chapter has two main sections with the
first part discussing how the different environmental variables were processed to produce
statistics for the regression analysis, which forms the second part of the chapter. However,
before delving into the finer details of the analysis, it is important to highlight some
assumptions which were made in the analysis.
Assumptions

Contemporaneity- the terraces were taken as belonging to the same period. This also
applies to the other archaeological features investigated like the pit structures.

The modern environmental parameters reflect the situation that obtained during the
time of the terraces was built. The modern physical environment is therefore a proxy
of the ancient environment.

The available data is a representative sample of the whole area.

The archaeological features of the Nyanga complex must have acted as a system not
as individual features. These archaeological features should also have a close
relationship with the surrounding environmental parameters.
33
4.2 Independent Variables
Elevation- The elevation map used for the production of elevation data was the DEM
downloaded from ASTER GDEM web site.
Slope- this was calculated from the DEM. This was done by running the spatial analyst
command in Arc Map. A decision was made on using log 10 of slope to avoid situations
where other values are squashed and others especially the large values which are normally
fewer are plotted way out when plotting on a distribution graph. The histograms illustrate
how the values can influence the distribution pattern.
Aspect- This refers to the direction in which the slope faces. The aspect affects other factors
which affect agriculture like the sunshine and winds hence a variable worth examining if it
played any significance in the location of terraces in Nyanga. The aspect was also calculated
by running surface analysis on the spatial analyst function. The process produced an aspect
map with a range of values of up to 360 degrees. There is lot of ambiguity in values from
aspect map for example 360 degrees and 0 are all facing north but if plotted on a distribution
graph these would appear as very different values. To make sense out of the aspect map, it
was however decided that on aspect, focus be on the south facing sides which receive more
sunshine in the southern hemisphere. Therefore for aspect, what was measured was deviation
from the south facing side. This was achieved using the raster calculator in ArcMap using the
formula; Abs([aspect map]-180). The absolute was used so that there would be no difference
on whether the deviation is towards the east or west. If the absolute was not used, the values
34
for the resultant map would have ranges from -179.9-179.9 with negative values as we move
eastwards to the north and the westwards becomes positive values.
Geology- The geology type considered was dolerite. Dolerite is the geology type suitable for
agriculture compared to granites available in the area. Soper (2002, 33) quoting Frobenius
noted that the terrace builders favoured dolerite rocks and as such the study aimed at
verifying and quantifying the claim. However, the analysis did not take the geology type as a
dummy variable where there is either presence or absence of the geology type. Instead,
geology was expressed as a continuous variable where distance „into‟ and „out‟ of dolerite
was considered. The boundaries between the different geology are presented on the paper
geology map as well demarcated, which is not the case on the ground (Soper-pers comm).
Thus the arbitrary boundaries results in a bias hence Soper (2002) notes that the boundaries
of the geology types are not as straight as portrayed in the geology maps. The advantage of
using continuous variables for such variables like geology is noted by Bevan and Connolly
(forthcoming) who states that “by expressing geology as a continuous variable in this way,
we are also able to avoid both the awkward use of dummy variables for geological categories
and any misleading edge effects otherwise occurring at geological boundaries” (Bevan and
Connolly forthcoming). It is therefore with this background that geology was not taken as a
dummy variable where there is presence or absence of dolerite but as distance hence the
geology map has been referred to as distance.
The distance map was created in ArcMap from the geology map. This was achieved by the
„Select by attribute‟ function and selecting the regrouped dolerite geology. The new dolerite
selection was exported as a shapefile, which was then converted to raster still in ArcMap. To
get the distance into and out of dolerite, the first step was the creation of lines from polygons,
a function available in Data Management- Features then polygon to polyline tools. Using the
Spatial Analyst function in ArcMap, the raster dolerite shape file was reclassified into „0‟,
other geology and „1‟, dolerite geology. From the Spatial Analyst again, straight distance
was calculated from the dolerite boundaries. The process produced a raster map with all
positive values, making it impossible to distinguish between distance „into‟ and „out‟ of
dolerite. Thus to make the distinction, the geology was reclassified again, and this time the
values were „1‟ dolerite and „-1‟ other geology types. Using the raster calculator, the
reclassified geology map was multiplied by the distance map and the result was a distance
35
map with positive values as one moves into dolerite and negative as one moves out of the
dolerite geology.
Figure 8 Distance measured from the dolerite boundary
36
4.3 Dependant variables
Terraces- These made the dependent variable. Their absence or presence was a key factor in
the analysis. The terraces were digitised as polygons from Soper (2002). The unit of analysis
was the cell as taking a polygon as one terrace would lead to unfair treatment of the terraces.
An example is when measuring distance from the terraces, if the polygon was taken as a
single unit; it would then leads to ambiguity as to where the distance could be taken from.
The digitised terraces were also converted to raster and the map was reclassified into „1‟
terraced land and „0‟ for the land without terraces. To do an analysis, there was need to create
a random sample which could be easily done in GRASS, using the ‘r.random’ command.
However, visually comparing the terraced land and that which is not terraced indicated that
the terraced is only a fraction of the total land. Using the GRASS ‘r.sum’ , the number of
cells were automated and these were 484 440 cells compared to the 2 982 435 cells of the
land not terraced. To do an analysis using the random terraces and the terraced land, it was
decided that the same amount of cells (484440) be randomly scattered in the land not
terraced. The idea was to see whether if the same number of terraces were randomly
scattered they would produce the same result. To create the random terraced land surface, the
terraced areas were given a null value and only the areas without terraces then would have a
chance of getting the random terraced cells.
Using the raster map calculator in GRASS the two maps, the terraced and the random
terraced areas were combined. The end result was a map coded 1(terraced), -1(random
terraced) and 0 areas without any of the two. This was reclassified using ‘r.reclass’ so that
the terraced land became1, random 0 and the rest became null and ready for analysis.
37
Figure 9 Map showing the terraces and randomly created non terraced areas
Settlement structures- These included the pit structures, double concentric enclosures and the
enclosures. These were digitised from Soper (2002)‟s maps.
38
4.4 Logistic Regression Analysis
In GRASS, the r.stats was run to produce statistics that were used for the regression analysis.
A logistic regression was done to find variables that would be useful in predicting the
location of terraces. Shennan (1988) defines a regression as a process where “we use an
independent variable to estimate the values of a dependant variable. The statistical analysis of
the data was all done in R software. For the regression model, the starting point was having a
null hypothesis, which in this case is; there is no correlation between the different variables
and terraces.
4.4.1 Univariate Logistic Regression Analysis
The univariate logistic regression analysis was done as a preliminary statistical analysis to
assess the correlation of each of the variables independently and terraces. This was done to
look at landscape attributes which are peculiar to terraced areas, in other words it was to
show if there is a distinction between terraced and non terraced areas. For each variable of
slope, distance, aspect and elevation, a regression model was produced to see the relationship
or correlation between the variable and the presence or absence of terraces. Models for each
variable were created using the „glm‟ function. For example the aspect model was created
using; asp180.model<-glm(terr~asp180, data=terraces, family=binomial(logit))
Summary of results
As preliminary analysis particularly visual analysis of the relationship of the different
variables with terraces, jitter plots of the observed data and the fitted regression line were
plotted and these are illustrated below. Geology, represented by distance is the only variable
showing a positive auto correlation where the number of terraces increases as the distance
into dolerite increases whereas the opposite is true for the other variables which all show
negative autocorrelation.
39
The distance plot also shows that while non terraces are almost evenly spread between -4km
to 3km, the actual terraces are concentrated between -3km and 1.5km. This gives a
preliminary clue as to how distance into dolerite geology, or geology influenced terracing on
the Nyanga plateau.
However, in the other plots of slope, aspect and elevation, there are no much differences in
distribution of terraces and non terraces.
40
Using the summary() function the statistics for each model were also produced. Of interest is
the p value which is far less than the 0.05 which means the variables can be taken as good
predictors of terraces. A chi square test was done to confirm the significance of the
relationship. This was easily done in R using the formula; 1-pchisq(Null Deviance-Residual
Deviance, 1). The chi square test result was „0‟ for all the variables indicating that the result
is statistically significant. Although the p value indicated that all the variables are good
predictors of terraces, there are some slight differences in the co relation coefficient with
variables like distance and asp180, showing greater values. This indicates that more
reliability of other variables than others in terrace prediction. The other difference noted is
whether the autocorrelation is positive or negative.
4.4.2 Multivariate Logistic Regression Analysis
The multivariate analysis was done as a further analysis of the univariate analysis. The
multivariate approach looks at all the variables and looks for the best combinations for
prediction of terraces. The same formula which uses the family logit was used only that for
this model, all the variables were added and the script below was produced;
mod<-glm(terr~log(slope)+asp180+elevation+distance, family=binomial(logit))
The result show again high significance for all the four variables. The assessment of the best
model was done using the stepAIC().The stepAIC is a function used to select a subset of
predictor variables from a larger set. The function however does not offer results in absolute
terms, instead it only shows results relatively. To use the stepAIC() function in R requires the
MASS package, so the first step towards achieving the choosing of the best fit model was
installation of the MASS package into R. After loading the MASS package, the stepAIC was
done using the model created from all the variables.
library(MASS)
mod2<-stepAIC(mod)
mod2$anova
summary(mod2)
The model was then tested against ANOVA, which has the basic function of showing
whether the model is statistically significant. The results of the process were then retrieved by
the summary(mod2) command. Summary of results is presented in the table below:
41
Estimate
Standard ERR
Z VALUE
Pr<|z|
Intercept
1.165
0.01042
111.81
<2e-16 ***
Log(slope)
0.05526
0.002170
25.47
<2e-16 ***
Asp180
-0.004146
0.00003974
-104.31
<2e-16 ***
Elevation
-0.0005061
0.000007287
-69.45
<2e-16 ***
Distance
0.0005855
0.000003815
153.46
<2e-16 ***
The stepAIC function retained all the variables from the initial model with a p value of far
less than 0.05 suggesting all the variables are significantly correlated with the terraces. The
results were suspicious given the very low p values. The variables were plotted against each
other to visually examine if the p value is influence by correlation among the variables
themselves.
The plots however indicated no correlation in any of the variables.
Using the results, it was then possible to produce a prediction surface, which is a map
showing relative probability of appearance of terraces on the landscape. The prediction
surface was created in GRASS using map algebra and results from the stepAIC function. In
GRASS, a log 10(slope) map was created. A logodds map was created by multiplying the
maps for the different variables used by the estimate and adding the intercept.
logodds=1.165+(log_slope*0.05526)+(asp180*-0.004146)+(dem*0005061)+(dist*0.000003815)
The resultant raster map was then used to get relative probability surface by using the
formula; relprob=(exp(logodds))/(1+(exp(logodds))) in raster calculator. This produced a
probability surface which is relatively useful in predicting where terraces are found. Each cell
on the map was accorded a cell value of the probability of finding terraces in that cell. The
highest relative probability of the surface is 0.8 which is very scarce. Though the prediction
surface lack very high predictive surfaces of maybe 0.9, it is quite useful as from enquiring
using the query tool in GRASS, most terraces are found in high probability areas though they
are others which fall in very low predictive surfaces. This indicates that the analysis managed
to yield some useful results.
42
Figure 10. The map shows distribution of terraces on the relative probability surface.
43
The relative probability surface was tested using the Kvamme‟s Gain statistics. Ideally
regeression models need to be tested in the field but as pointed out by Vaughn and Crawford
(2009) there are several constraints to this approach and the result is the use of quantitative
methods to validate the regression model. In this case Kvamme‟s Gain statistics which is one
of the validation methods used in archaeology has been employed. The Kvamme Gain
statistic is based on the assumption or null hypothesis that by chance, one would expect to see
proportion of the land at a certain probability being the same as the proportion of features
being investigated.
To calculate the Kvamme‟s Gain statistics, four relative probability classes were defined (0.2,
0.3, 0.6 and 0.7). Maps for these classes were created in GRASS using raster calculator with
the input raster layer being the relative probability surface and putting the formula; A>=0.3.
INSERT MAPS---O.6 and 0.2
The number of land cells covered by the different probability surfaces was obtained from the
r.stats in GRASS. The percentage was then calculated in excel. The GRASS r.coin produced
the number of actual terrace cells in each map and out of GRASS, in excel, the percentage
was also calculated. The Kvamme‟s statistics was calculated using
G = 1 – S/O
Where S is the percentage of the total area where terraces are predicted and O is the
percentage of observed terraces. The table summarises the results:
Relati
1
0
total
%
ve
#of
total
%terrs
G
terrs
terrs
47818
48214
99.1778 0.00067
0
4
4
46266
48214
95.9595 0.03048
3
4
1
72972
48214
15.1349 0.33749
statistic
prob
0.2
27411111
24597900
00
0.3
0.6
0.7
27657090
99.1
00
25730487
19266030
27657090
00
0
00
27731790
24883911
27657090
0
00
00
7646400
27580626
27657090
00
00
93.03
10.02
4
0.27
1742
8
7
1
48214
0.36130 0.23479
4
3
3
44
The utility of the prediction is measured between -1 and 1 where if gain statistic is close to 1,
then the model has increased predictive utility and if near 0, then little utility and if on the
negative then it is a reverse predictive utility (Kvamme 1998). The results indicate that
although the regression model does not have a reverse predictive utility, it has very little
predictive utility. At 0.6 relative probability having the highest utility.
Figure 11. Map showing large areas of ‘yes’ at 0.3 probability
45
Figure 12. Probability surface at 0.6 probability
Further analysis to see how the terraces are related to the variables was done and this
involved the analysis of residuals known as kriging of residuals. Plotting the residuals is
regarded as a cheap and effective way of assessing model performance and specification
(Bevan and Connolly upcoming). A variogram, which is defined as “a tool that quantifies
spatial correlation was used to plot the residuals” (http://www.statios.com/Resources/04variogram.pdf). In analysing the residuals it is expected that the variance of the residuals
should remain constant. A variogram as noted by Zabel and Hensen (2006) can be
implemented when one wants to find whether results show spatial correlation. Variograms
are also intended to identify trends where structures shows “decreasing similarity with
distance”, that is closed neighboured values are expected to be similar than distant ones.
The gstat() package which has the variogram function was loaded into R but the data was too
big and the variogram option could not be executed. The kriging of residual is one of the
reliable methods of testing models and as such, to make the variogram executed, it was
decided that a sample be taken from the large data set. 10 percentage of the terraces and non
terraces be analysed and then the variogram was plotted. The sample was taken by running
the r.random in GRASS, and 10% from the terraces and the same percent from the non
terraces. The sample was exposed to the same processes of regression analysis of the larger
data set and further analysis of the kriging of residuals was successfully done. To make sure
that the sample is representative of the larger sample, the same process of running r.random
and taking a percentage of the larger data set was repeated three times and the results were
almost similar hence discussed once. The variogram plot indicate that the relation between
individual values diminishes with distance, and the range is around 6km where the values
become almost constant
4.4.3 Linear Regression
Data for the linear regression was based on Soper (2002, 33) map where the study area is
divided into 10km grids. In each grid the number of the different pit structures is represented
by symbols. To obtain this data into GIS, the map was georeferenced and then grids were
digitised into vector polygon grids, all done in ArcMap. In the attribute table for the grids
dataset fields for each of the structure types was added. The structures in the map are
represented by symbols which have a range of values and for this analysis, the top most value
46
was taken to represent the number of structures in the grid. For example in the 20-40 range,
in the attribute table, 40 was used. Additional data was added into this attribute table and this
included amount of terraces in each grid. The amount of terraces in each grid was obtained
using zonal statistics in ArcMap, where the input map will be the grids and then the raster
map being the map where terraces are coded „1‟ and non terraced is „null‟. The same method
was used to obtain amount of dolerite which provides the most agricultural productive
geology and in this case the raster value was the raster version of the dolerite with dolerite
areas coded „1‟ and all other geology coded „null‟. The table for the zonal statics was joined
with the attribute table for grids using the joins and relates function in ArcMap and only
relevant statistics was added to the main attribute table and in the case for terraces and
geology, it was the „sum‟. Using the zonal statistics function, the mean of log slope, standard
deviation of log slope and mean of the aspect was obtained. The standard deviation of log
slope looks at how far the slope deviates from the mean. The attribute table was exported as a
as a dbf. The dbf was opened using excel which was then saved as a csv comma delimited
which would read easily into R using;
>structures<-read.csv("structures.csv").
A linear regression was done to show the correlation between the pits and the terraces,
geology, slope and aspect. These variables were the independent while the structures were the
dependent variable. The pits were taken as representing the settlement sites hence in the
analysis, no effort was made to separate the different types (double concentric rings,
enclosures and pit structures). For the linear regression, the unit of analysis was the grid. The
geology was measured as the amount of dolerite in each grid, which in turn was turned as a
percentage due to uneven sizes of the grids. The terraces were also calculated as a percentage
which was obtained by dividing the total number of cells in each grid by the actual amount of
cells with terraces in the grid multiplied by 100.
A preliminary analysis of looking into the relationship between the pits and the variables was
done by using scatter plots. A simple linear regression model was fit on the scatter plots.
47
The scatter plots show a fairly good fit for pits and terraces, geology and mean of log slope
and a poor fit between the pits and standard deviation from mean of log slope and the mean
of aspect. The scatter plots show a positive auto correlation where for example in the case of
pits measured against terraces, one finds enclosures closer to the terraces area. Further
analysis was done using the summary() function of residuals. Summary of results from the
summary of res for each variable is shown in the table below.
48
Terraces
Estimate
Geology
Slope
– Slope-std
mean
deviation
aspect
0.09487
-0.06297
8.485
4.278
0.01448
p-value
0.0547
0.173
0.0552
0.658
0.847
Residual
8.945
9.011
8.945
9.071
9.077
0.01479
0.0291
0.001573
0.0002973
0.006909
0.02133
-0.006414
-0.0077
1.877
3.746
0.197
0.03718
coefficient
Sta. Error
Multiple R- 0.02922
squared
Adjusted R- 0.02146
squared
F-statistic
3.763
Table is a summary of results of pits measured against different variables
From the results, the negative geology estimate coefficient of -0.06297 confirms the scatter
plot which shows a negative autocorrelation where number of pits decreases as the
percentage of the dolerite geology increases. The p values for mean of log slope and terraces
against the pits are very low, less than „0.05‟ the usual minimum accepted to reject the null
hypothesis. This suggests that using mean of log slope and terraces, the null hypothesis that
there is no correlation between pits and the two variables can be rejected. The p-value for
geology on the other hand, even though exceeding the 0.05 limit is not very far away and as
such it can be used to also reject the null hypothesis though with a greater error margin. The
p-value for the standard deviation of log slope and aspect seems to confirm what can be
visualised from the scatter plots where the fitted model is almost zero hence showing no
autocorrelation between the pits with neither aspect and the standard deviation of log slope.
The multiple R-squared and the adjusted R-squared being measures of how strong are the
relationships. The r-squared is the estimate coefficient squared and if it is close to 1, then the
independent variable explains close to 100% of the variation in the dependent variable, but
for this analysis both the multiple R-squared and the adjusted which take into consideration
the degrees of freedom are very low, which explains a very little percentage of the variation.
49
To examine the reliability of the regression analysis, a further analysis of the residuals of the
models (mean of log slope) was done. The first step was to compute the standard residuals
and this was done using
sr<-residuals(res)/residual standard error
The standard residual was then plotted against the covariate.
Ideally the scatter plot should be random, but the distributions for the standard residuals of
mean of log slope and the pits shows a pattern which suggests there are other explanations for
the relationship.
This chapter has discussed the analysis taken to see the relationship between terraces and the
physical environment and also with the settlement structures on a large scale. The results
indicate that there is a level of correlation either positive or negative between terraces and
these variables at large scale. The next chapter however, looks at a smaller scale, to see if
there is still correlation between the terraces and the variables on a finer resolution.
50
CHAPTER FIVE: ANALYSIS AT SMALL SCALE
5.1 Introduction
This chapter is on the analysis which was done at a finer resolution. The area investigated is
in Nyangui forest and Soper (2002) refer to it as Nyangui Block G. The area covers
approximately 7km by 5km was chosen for further analysis. This is the area referred to as
Nyangui, block G in Soper (2002) with detailed archaeological features, terraces included and
in addition the area had some contours with 20m spacing which would suggest that compared
to the ASTER DEM with a cell size of 30m used for the large scale analysis, for the finer
resolution DEM, the cell size was reduced to 20m hence finer resolution. The hard copy map
was scanned, georeferenced and different features were digitised in ArcMap for them to be
integrated into GIS.
Figure 13. Nyangui Block G
51
5.2 Variables
The same variables of slope, aspect, elevation and geology which were used for the regional
analysis was used for the small scale. The elevation data were obtained from a DEM that was
created using the contours. The creation of DEM involved digitizing of the contours and then
the digitised contour vector file in ArcMap was imported into GRASS using the r.in.org.
Once in GRASS, contours were converted to raster v.to.rast. Using the raster version of the
contours, the interpolation was done using the r.suf.contour. This DEM confirmed accuracy
of the data from Soper (2002)‟s work since the DEM is so close to the one acquired from
ASTER GDEM for the same region as illustrated below with slight differences obviously as a
result of the different cell size.
Figure 14. Nyangui DEMs
52
Running the surface analysis on the DEM produced the other variables of slope and aspect.
For slope log 10 of the slope was used and for the aspect, it was still south facing slopes were
considered. To get south facing slopes and how far other slopes are deviating from the
direction map calculator in ArcMap was used. Using the absolute (aspect map)-180, the
resultant map produced values showing how far each slope is deviating from south.
The geology data, which is the distance in and out of dolerite, was obtained from the larger
scale geology data. The larger geology map was clipped to fit the Nyangui block G using the
clip function in ArcMap.
The terraces were digitised as polylines and for the purposes of producing statistics as to how
much of the land is covered by the terraces, polygons were created around the polyline
terraces.
Figure 15. Nyangui terraces and sites
53
5.3 Univariate Regression Analysis
The statistics for the regression analysis were obtained using the r.stats in GRASS. Included
in this finer resolution analysis was distance to pit structures. The univariate regression
analysis was meant to assess the relationship between each variable and the terraces.
Preliminary analysis included plotting of jitter plots.
The aspect jitter plot indicate that the non terraces represented by the 0.0 there is almost an
even distribution while the terraces indicated by 1.0 on the y-axis are less on the south facing
slopes and as we move away from south facing slopes the more the terraces hence a positive
autocorrelation shown by the modelled red line. The elevation jitter plot on the other hand
shows that terraces are concentrated between 1200m and 1800 m above sea level. However,
there is no clear correlation between the two, judging from the modelled regression which is
almost a straight line.
54
The correlation between geology and distance to pit structures all shows negative
autocorrelation. For the distance to pit structures, the jitter plot indicate that terraces are
found within much closer distance to pit structures (between 0 and 1000m) and as we move
further from the pit structures there are rarely any terraces. For the geology, the indication is
terraces are found within a distance of 1km from the dolerite geology. The negative
autocorrelation found in the geology variable for the small scale analysis is a direct contrast
to what the regional analysis suggested. At regional level, there was clear positive
autocorrelation while the finer analysis indicates a clear negative correlation.
Summary of results
Elevation
Slope
Aspect
Distance_geol Distance_pit
Estimate
-6.840e-05
0.12322
0.0030316
-1.759e-03
-2.610e-03
Standard
7.427e-05
0.06437
0.0005231
4.649e-05
7.279e-05
Z value
-0.921
1.914
5.795
-37.84
-35.85
p- value
0.357
0.0556 .
6.82e-09 ***
<2e-16 ***
<2e-16 ***
Error
As shown in the table, the variables have different p-values with geology and pit structures
having the lowest p-values suggesting using these variables individually, the null hypothesis
can be rejected, meaning geology and distance to pit structures can be used with some level
of confidence as predictors of terraces. To some extent, slope can also fall within this
category as its p value is slightly above the 0.05 threshold. Elevation and aspect however,
have a very high p-value hence the null hypothesis cannot be rejected using these variables.
The results for the small scale show discrepancy between the two scales of analysis.
55
5.4 Multivariate regression
The multivariate analysis performed by the stepAIC function retained all the variables as
good predictors of terraces with a very low p-value. The prediction surface was also created
and this relatively fit the model where terraces are most found in high probability surfaces.
56
CHAPTER SIX: DISCUSSION AND CONCLUSIONS
The results have shown some autocorrelation between terraces and the variables considered
for the study both at a courser and finer resolution. This has been statistically proven where
the multivariate regression analysis, the use of stepAIC retained all the variables. This
correlation only varies in its strength. An example is pronounced geology variable which
shows a much noticed positive autocorrelation at large scale. This confirms claims by Soper
(2002, Chirawu, 1999) that terraces favour dolerite geology in the geology. However, at a
more localised level, which in this study is the small scale analysis, some discrepancies on
the correlation between large scale and the smaller scale are noted. Whereas, in large scale
analysis, the geology variable has a positive correlation, the opposite is true for the finer
resolution. This suggest that looking at the area as a region, the more one gets into dolerite,
the more one expects to find terraces while at the smaller scale the more one gets into
dolerite, the less likely that terraces will be found. Be that as it may, conclusions could not be
drawn particularly drawing of conclusions that at a smaller scale there is negative correlation
of terraces and distance into dolerite due to sample representativeness. Admittedly, more than
one of the smaller scale samples could have been done. This therefore suggests that the
current study is only the beginning of more spatial analysis of the archaeological complex.
The other variable of aspect shows a negative autocorrelation both at large and small scale
analysis, suggesting that more terraces are on south facing slopes. The more deviation from
the south facing slopes, the less likely one expects to get terraces. It has been emphasised in
regression models that correlation should not be confused with causation. That is even if
there has been recorded level of correlation between terraces and the different environmental
factors, it does not automatically suggest that they were put into consideration in the location
of the terraces. However, the regression analysis has indicated that there is a pattern followed
in the location of terraces and from the patterns, one can deduce meaning.
There are however, a number of challenges which were encountered in the process of
carrying out the regression analysis, which might have influenced the results. Like many GIS
projects, the results of analysis could have been affected by edge effects, especially
considering that the analysis left out some area due to unavailability of data. In the case
where artificial boundaries were created in the form of grids, the problem of edge effects
57
should also be highlighted. False patterns can be created which do not represent the real
situation. Another example is when distance „in‟ and „out‟ of dolerite was calculated, the
terraces on the edges obviously were unfairly treated. Another factor affecting results could
be from the regression analysis of large data sets. The very low p- values indicating that the
correlation was statistically significant could have been as a result of the large data sets.
Related to this challenge is besides being too big a data set, there were clumped spatially that
is lots of „1s‟ together. For regression model, this is a real problem as regression analysis has
always been used to deal with small numbers which would be spatially widespread for
example sites. This therefore means that in future there is need of looking at how such data
sets can be dealt with in regression models.
Besides shedding light on how the different environmental variables could have shaped the
pattern of terraces of Nyanga, the study has also successfully yielded a predictive surface
which can be used in surveys in those areas yet to be archaeologically investigated. With
terracing still being practiced, it would be a worthwhile project to see how the present terrace
systems integrates with the ancient ones hence bringing archaeology close to the
communities.
58
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Sutton, J.E.G. 1984. “Irrigation and soil conservation in African Agricultural history with a
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61
APPENDIX
Appendix 1. Part of geology attribute table
cat
17
18
18
18
40
60
40
40
97
171
263
267
318
321
325
326
367
372
372
393
402
424
431
431
431
431
431
431
431
431
431
Id
30
3
33
3
32
47
32
32
38
41
3
3
3
46
39
3
3
39
39
4
25
47
3
3
3
3
3
3
3
3
3
type
basec_gd
dolerite
basec_gdh
dolerite
basec_g
quarts
basec_g
basec_g
basec_gth
basec_st
dolerite
dolerite
dolerite
basec_e
basec_gth
dolerite
dolerite
basec_gth
basec_gth
dolerite_dp
basec_gt
quartz
dolerite
dolerite
dolerite
dolerite
dolerite
dolerite
dolerite
dolerite
dolerite
type_group
post_admalite_granites
dolerites
intermediate_granites
dolerites
adamalites_granite
adamalites_granite
adamalites_granite
old_tornalites_gneiss
granite_incusions
dolerites
dolerites
dolerites
granite_incusions
old_tornalites_gneiss
dolerites
dolerites
old_tornalites_gneiss
old_tornalites_gneiss
dolerites
minor_granite_intrusions
dolerites
dolerites
dolerites
dolerites
dolerites
dolerites
dolerites
dolerites
dolerites
regroup
7
2
9
2
8
12
8
8
10
11
2
2
2
11
10
2
2
10
10
2
4
12
2
2
2
2
2
2
2
2
2
area
22269.45754790000
9384.80089826000
293974.45847600000
29068706.69530000000
25090.27032700000
25090.27032700000
106066.93807700000
3398193.64763000000
14862.60704010000
18410.20517270000
53284.76929840000
487154.83186600000
13414.49803480000
7356.20967416000
640578.35376100000
1948488.31044000000
41548.14875800000
28410.04478730000
311225.68980500000
17175.94986180000
22070.67663790000
11896.61544130000
31411.12795490000
4943.65334023000
16024.15503630000
47357.18022970000
38284.57716580000
24474.36050730000
21656.74385930000
180262.66817100000
11723.73655090000
62
Appendix 2 . Attribute table for grids used for the linear regression
Id
pits
enclos d_encl pits_all count1 terrs_1 perc_terrs
1
0
0
0
0 30738
321
1.04431
2
0
0
0
0 29114
4115 14.13409
3
0
0
4
4 29250
17612 60.21197
4
20
0
0
20 28871
6476 22.43081
5
0
0
0
0 29190
1648 5.645769
6
0
0
0
0 28088
0
0
7
0
0
0
0 30247
7663 25.33474
8
0
0
0
0 30802
1398 4.538666
9
0
0
0
0 29811
10308 34.57784
10
0
0
0
0 30429
13029 42.81771
11
50
0
0
50 28600
5115 17.88462
12
40
0
0
40 29435
13620 46.27145
13
0
0
0
0 30584
11486 37.55558
14
0
0
0
0 29165
0
0
15
0
0
0
0 29161
756 2.592504
16
0
0
0
0 28683
750 2.614789
17
0
0
0
0 30590
766 2.504086
18
0
0
0
0 30481
0
0
19
0
0
0
0 29613
3101 10.47175
20
5
0
0
5 29774
1750 5.877611
21
0
0
0
0 28577
0
0
22
0
0
0
0 29956
6792 22.67325
23
0
0
0
0 29175
12630 43.29049
24
5
0
0
5 30266
0
0
25
0
0
0
0 29413
0
0
27
0
0
0
0 31734
898 2.829772
geolo perc_geol lslopem lslopes
aspmea
463 1.506279 0.755365 0.260092 96.0674
3658
12.5644 0.989215 0.32114 95.7535
1516 5.182906 0.839354 0.255037 70.288
536 1.856534 1.17773 0.240786 106.653
4376 14.99144 0.963512 0.304047 97.3408
2181 7.764882 0.89817 0.244693 85.6098
5321 17.59183 0.974208 0.313657 97.4112
1822
5.9152 0.522399 0.183075 99.8175
6640 22.27366 0.785463 0.279071 77.5271
12497 41.06937 0.974012 0.315772 96.8485
933 3.262238 1.09512 0.25972 102.559
1575 5.350773 1.13557 0.230394 95.803
6448 21.08292 1.02177 0.329624 81.8169
4380
15.018 1.07609 0.284623 102.77
1146 3.929906 0.912739 0.336241 95.2561
184 0.641495 0.634473 0.18301 87.9456
241 0.787839 0.685803 0.283661 82.3712
64 0.209967 0.73405 0.339698 108.602
1506 5.085604 0.644375 0.20397 96.2228
16315 54.79613 1.08421 0.265188 108.272
9788 34.25132 0.987545 0.241938 94.9183
4768 15.91668 0.782137 0.231802 103.322
10561
36.1988 0.78947 0.242652 105.448
21386 70.66015 1.00915 0.219109 107.792
1369 4.654405 1.19307 0.227972 103.891
206 0.649146 0.961473
0 96.4873
63
29
31
33
35
37
38
39
40
41
42
43
44
45
46
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67
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250
25.40334 9411
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99
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66
130
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67
Appendix 3 Commands for statistical analysis
In GRASS:
r.stats input=”reclas_perc”,”slope”,”asp180”,”dist” fs=”|” output=sites.txt –n – 1 -g
In R.
install.packages ()
header<-c(“X”,”Y”,"terr","elevation","slop","asp180","distance")
sites<-read.table("sites.txt", header=FALSE, col.names=header,sep="|")
terrs<-subset(sites,terr==1)
non.terrs<-subset(sites,terr==0)
attach(sites)
slope<-slop+0.00001
elevation.model<-glm(terr~elevation, data=sites, family=binomial(logit))
asp180.model<-glm(terr~asp180, data=sites, family=binomial(logit))
distance.model<-glm(terr~distance, data=sites, family=binomial(logit))
slope.model<-glm(terr~log10(slope), data=sites, family=binomial(logit))
1-pchisq(null-residual deviance,1) =0 for all my datasets
#Jitter plots
plot(elevation,jitter(terr,.1),ylim=c(-0,1),ylab="")
par(new=TRUE)
plot(elevation,fitted(elevation.model),col=2,pch=3,
ylim=c(-0,1),ylab="Terr Presence")
plot(log10(slope),jitter(terr,.1),ylim=c(-0,1),ylab="")
par(new=TRUE)
plot(log10(slope),fitted(slope.model),col=2,pch=3,
ylim=c(-0,1),ylab="Terr Presence")
plot(asp180,jitter(terr,.1),ylim=c(-0,1),ylab="")
par(new=TRUE)
plot(asp180,fitted(asp180.model),col=2,pch=3,
ylim=c(-0,1),ylab="Terr Presence")
plot(distance,jitter(terr,.1),ylim=c(-0,1),ylab="")
68
par(new=TRUE)
plot(distance,fitted(distance.model),col=2,pch=3,
ylim=c(-0,1),ylab="Terr Presence")
mod<-glm(terr~log(slope)+asp180+elevation+distance, family=binomial(logit))
library(MASS)
mod2<-stepAIC(mod)
mod2$anova
summary(mod2)
#Now fitting this model to the datapoints
fitmod2<-fitted(mod2)
fitmod2.xy<-cbind(sites,fitmod2)
fitmod2.xy$resid=fitmod2.xy$terr-fitmod2.xy$fitmod2
write.csv(fitmod2.xy, file = "sites_fitmod.csv", row.names = FALSE)
library(gstat)
coordinates(fitmod2.xy)<-~X+Y
g1 <- gstat(id="res", formula=resid ~ X+Y, data=fitmod2.xy)
v2<-variogram(g1, cutoff=1000, width=50)
plot(v2)
dev.print(device=pdf, "v2.pdf")
69