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Exercise

1. Travel time to Rotterdam harbour Average travel time to nearest 100 000 jobs Travel time to nearest highway entrance Average travel time to nearest 500 000 inhabitants Average travel time to nearest 100 000 inhabitants Travel time to the nearest main forest water area frequently used for recreation A scenario for the CLUE S model consists of a file with land requirements and a file that indicates areas where restrictions to conversion apply Three scenarios for land use requirements demand are available The first scenario file demand in1 is based on the assumption of moderate urbanisation for the period 1989 till 2008 An increase in the demand of 1 5 per year is expected for the land use types greenhouses residential areas industrial commercial recreation and airports The demand for the land use types arable land and forest nature will remain constant and a decrease is expected for other agriculture and grassland The second scenario file demand in2 is based on the assumption of a strong urbanisation in the period 1989 till 2008 The demand for the land use types residential area industrial commercial recreation and airports increases strongly with 3 per year This results in a decrease of the land use types other agriculture grassland and arable land The demand for the land use types nature forest and greenhouses is expected to remain equal The third scenario file demand in3 will be used for validation of the model Thi
2. it was terminated in the course of the 1980s More and more credence was given to the idea that business locations and residential areas should be planned as close as possible to existing cities This would lead to more people travelling by public transport rather than by car and provide more opportunities to keep the Green Heart and other open areas green These ideas formed the basis of the Fourth Policy Document on Town and Country Planning in the Netherlands which was published in 1988 and revised in 1990 after a change of government The VINEX as the revised document is known designated some twenty locations in which most of the Randstad s new housing will be built in the period up to 2005 the VINEX locations All of these locations are situated just outside the official boundaries of the Green Heart B The role of the Green Heart in current planning The Green Heart was originally a somewhat vague planning concept It was an area with no clear boundaries in which a number of things were supposed not to happen There were to be no new houses greenhouses or roads But these were exactly what were built there The Green Heart not only became less and less green it also shrank because certain regions were no longer regarded as being part of it These included the Little Ring between The Hague Delft Rotterdam and Zoetermeer a major part of the Haarlemmermeerpolder where Schiphol Airport is located and the area around the villages of Vl
3. or negative e g population density with forest nature Table 7 1 presents the available ArcView ASCII grid files of the land use types and location factors These files can be imported in ArcView through File Import Data Source ASCII Raster Table 7 1 Available land use type and driving factor files Code Filename Description 0 Cov0 0 Other agriculture 1 Cov1 0 Grassland 2 Cov2 0 Arable land 3 Cov3 0 Greenhouses 4 Cov4 0 Residential areas 5 Coc5 0 Industrial commercial 6 Cov6 0 Forest nature 7 Cov7 0 Recreation 8 Cov8 0 Airports 0 Sclegr0 fil Population density 1 Scler1 fil Elevation 2 Scler2 fil Groundwater level 3 Scler3 fil Distance to water 4 Scler4 fil Organic matter content 5 Scler5 fil Calcium content 6 Scler6 fil Clay content 7 Scler7 fil Travel time to station 8 Scler8 fil Travel time to airport 9 Scler9 fil Travel time to harbour 10 Scler10 fil Travel time to work 11 Sc1gr11 fil Travel time to highway 12 Sc1gr12 fil Travel time to city 500 000 inhabitants 13 Sc1gr13 fil Travel time to town 100 000 inhabitants 14 Sc1gr14 fil Travel time to recreation gt Prepare for each land use type a hypothesis of the potential location factors from Table 7 1 that might influence the suitability of a location for that land use type Select for each land use type 3 to 5 driving factors As help you can import the current land use map cov_a 0 and the driving factors in ArcView to see their spatial dis
4. 15675 19253 2057 25413 125979 16963 12761 45110 15977 15722 19615 2084 24650 124862 17044 13124 45670 16410 15769 19984 2112 23911 123685 17126 13496 46236 16855 15817 20360 2140 23194 122448 17208 13880 46809 17311 15864 20743 2168 22498 121151 17291 14274 47390 17780 15912 21133 2197 21823 119794 17374 14679 47977 18262 15960 21530 2226 21168 118376 17457 15096 48572 18757 16007 21935 2256 20533 116898 17541 15525 49174 19266 16055 22347 2286 19917 115358 17625 15966 49784 19788 16104 22767 2316 19320 113757 17710 16419 50402 20324 16152 23195 2347 18740 112094 17795 16886 51027 20875 16200 23631 2378 18178 110368 17880 17365 51659 21440 16249 24076 2410 Save this file in the installation directory as demand in where can be defined by the users e g demand in4 Restart the CLUE S model it is now possible to select the new land requirement file and simulate the land use changes Analyse the results Exercise CLUE 3 Spatial policies area restrictions This option indicates areas where land use changes are restricted through spatial land use policies or tenure status Maps that indicate the areas for which the spatial policy is implemented must be supplied Some spatial policies restrict all land use change in a certain area e g when in a forest reserve all logging is banned Other land use policies restrict a set of specific land use conversions e g residential construction in designated agricultural areas In this exercise we
5. 2008 However this growth should take places in the areas designated for the ecological main structure EHS gt Run the simulation for the scenario with strong nature development in the EHS areas Follow the steps below e Adapt the land use requirement file demand in1 as such that a gradual increase with 15 000 ha of forest nature at the expense of grassland and arable land is simulated for the period 1989 2008 Make sure that the total demand for all land use types remains equal e Import the file with the EHS areas region_ehs fil in ArcView File Import Data Source ASCH Raster e Reclassify the 9998 values into 1 Analysis Reclassify Unique to create a file that indicates areas where the specific land use conversions are allowed e Save the theme by Theme Save data set and give the date set a name e Export this data set map to an ASCII file File Export Data Source ASCII Raster and name it sel gr1 5 fil e Edit the conversion matrix as such that the conversion to forest nature is only allowed in areas of the EHS e Adapt the main parameter file wain 1 by changing the number on line 4 which indicates the number of driving factor files e Run the simulation with region fi and the adapted demand file gt Run the simulation again with the same demand scenario but now without the area restrictions for the conversion to forest nature edit the conversion matrix gt Analyse the results and compare t
6. Ecological modelling 116 45 61 Verburg P H Soepboer W Veldkamp A Limpiada R and Espaldon V 2002 Modeling the spatial dynamics of regional land use the CLUE S model Environmental Management 30 3 391 405 Verburg P H and Veldkamp A 2004 Projecting land use transitions at forest fringes in the Philippines at two spatial scales Landscape Ecology 19 1 77 98 Costanza R 1989 Model goodness of fit a multiple resolution procedure Ecological Modelling 47 199 215
7. growth of other smaller towns and villages was to be curbed by restricting the number of new homes for which planning permission would be given At the same time urban regeneration projects would be launched to bring migration from the cities to a halt In the third instalment the Policy Document on Rural Areas which was published in 1977 attention focused for the first time on the Green Heart s function not only as an agricultural centre but also as a location for leisure pursuits and nature conservation 6 In the 1980s it became evident that these policies were yielding some satisfactory results The populations of the major cities had stabilised and large numbers of new homes had been built in the growth centres Without these growth centres there would have been more urban sprawl and an even greater increase in road traffic But the policymakers had overestimated the extent to which they could influence companies in their choice of location Most companies were inclined to locate their operations within the Randstad near Schiphol Airport for instance and this led to very dense commuter traffic to and from the growth centres This has led to problems in and around Amsterdam in particular where every morning and evening motorists queue to pass through the tunnels under the IJ and the North Sea Canal on their way to and from the residential areas to the north of the city Though policy on growth centres was by no means regarded as unsuccessful
8. must be summarized A weighted average of the fits at different window sizes is a possible way of summarizing the overall fit that allows more weight to be given to smaller window sizes while not totally ignoring the large window sizes For this purpose the following formula can be used n k w l 2 Fe F w l t n 2 ar w 1 w l F is a weighted average of the fits over all window sizes F the fit for sampling windows of linear dimension w amp a constant and w the dimension of one side of the square sampling window The value of amp determines how much weight is to be given to small versus large sampling windows A default value for k 0 1 gt Calculate the weighted average of the fits F for your results based on the formula above The calculated fit of goodness of the model is only a relative value that can be used to compare the results of different simulations However the model performance should be compared because a large pattern is always simulated correctly It should also be compared with a certain default especially for simulations where not many cells are changing In that case a high value for F can be obtained without giving information about how good the model is Therefore you have to compare the calculated fit with the fit of a certain null model for example the initial conditions This indicates if the model has any additional value gt Run the validation procedure but now for the initial la
9. sampling windows in the scene for window size w A small program has been developed to calculate these fits for different window sizes This program validation exe can be found in the installation directory of the CLUE S model First the observed map and the simulated map which should be as files have to be selected The cell size and extent of these files should be exactly the same Then the number of land use types the step size of the expanding window and the maximum window size have to be indicated Figure c8 1 After running the procedure an output file output txt is created it contains two columns with the window size and with the calculated fit gt Run the validation program for your simulation result of exercise 7 First rename the file cov_all 7 to pred asc Open validation exe and select observed map cov1996 asc and simulated map pred as Set the number of land use types at 9 step size at 4 and maximum window size at 40 Then click Validate to run the procedure Copy the results of output txt to Excel and make a graph of the results Multiple Resolution Procedure ey _ ol x CLUE Validate select input Validation tool Number of LUT Observed map Simulated map p S cov1996 asc Step size Max window 40 pred asc h Figure c8 1 Validation program with multiple resolution procedure To determine an overall degree of fit between two maps the information in the plot of window sizes versus fit
10. term Randstad for more than just the ring of towns and cities in the area Since the rural areas have increasingly been feeling the impact of urban sprawl with more traffic on the roads more people engaged in leisure pursuits and encroaching suburbanisation the trend is now to refer to the whole area the cities and their immediate vicinity and the Green Heart as the Randstad A second characteristic of the Randstad is the fact that it has more than one centre None of the cities can claim to be the undisputed capital There is no hierarchy since the four major cities Amsterdam Rotterdam The Hague and Utrecht each fulfil a separate role Amsterdam might be the capital of the Netherlands but it is not the seat of government It is however the main financial and cultural centre Rotterdam has the largest port not only in the Netherlands but also in the world The Hague is the seat of government and thanks to its central location Utrecht is the hub of the rail system and a conference and trade fair centre Most of what is said about Randstad Holland in the above text still applies But nearly 20 years on the situation with regard to the Green Heart has changed somewhat To start with the Ministry responsible for land use planning gave it official borders in 1990 which means that exact figures can now be provided on surface area and population of this part of the Randstad What is more the Green Heart Administrative Platform was se
11. the Green Heart still gives rise to controversy According to some critics of policy the whole concept of the Green Heart is nothing but a myth kept alive these forty years which should be abandoned if Randstad Holland is to maintain its position as an international metropolis In their view the agriculture sector in Europe is too weak to act on a large scale at least as the support base of a green revolution Instead nature conservation and recreation areas should be planned only in those locations which are best suited to this purpose while the rest of the Green Heart should support the urban functions of the Randstad To hear the various views on the subject the Ministry of Housing Spatial Planning and the Environment held a round of talks with all the parties involved in late 1995 These included local authorities water boards representatives of the business community and organisations involved in for example leisure activities and nature conservation From these talks it became obvious that there is still a broad support base for land use policies that enhance the green quality of the Green Heart This will therefore be the objective of land use planning into the next century Introduction to the CLUE model The Conversion of Land Use and its Effects modelling framework CLUE Veldkamp and Fresco 1996 Verburg et al 1999 was developed to simulate land use change using empirically quantified relations between land use and i
12. 050 11 1998 21244 130704 16707 12348 45498 15814 5675 19587 2050 12 1999 21031 129920 16623 12409 46044 16051 5675 19822 2050 13 2000 20821 129120 16540 12471 46596 16292 15675 20060 2050 14 2001 20613 128304 16457 12534 47155 16536 5675 20300 2050 15 2002 20407 127473 16375 12596 47721 16784 15675 20544 2050 16 2003 20203 126625 16293 12659 48294 17036 5675 20791 2050 17 2004 20000 125760 16212 12723 48873 17292 5675 21040 2050 18 2005 19800 124879 16131 12786 49460 17551 15675 21292 2050 19 2006 19602 123982 16050 12850 50053 17814 5675 21548 2050 20 2007 19406 123067 15970 12914 50654 18082 15675 21807 2050 21 2008 19212 122136 15890 12979 51262 18353 15675 22068 2050 2 e When all values have been defined select the values without land use type names and year numbers and paste the contents into a text editor e g Notepad Insert a line at the top of the file with the number of lines years for which the land requirements are specified 20 in our example fj demand in4 Notepad File Edit Format Help 32425 132775 16325 10200 40875 12900 15350 16900 1875 31452 132135 16403 10490 41382 13250 15396 17218 1900 30509 131434 16482 10788 41895 13609 15442 17541 1925 29593 130674 16561 11094 42414 13977 15489 17871 1951 28706 129854 16641 11409 42940 14356 15535 18207 1977 27844 128975 16721 11733 43473 14745 15582 18549 2003 27009 128036 16801 12066 44012 15145 15628 18898 2030 26199 127037 16881 12409 44558 15555
13. Line Description Format 1 Number of land use types Integer 2 Number of regions Integer 3 Max number of independent variables in a regression Integer equation 4 Total number of driving factors Integer 5 Number of rows Integer 6 Number of columns Integer 7 Cell area Float 8 xll coordinate Float 9 yll coordinate Float 10 Number coding of the land use types Integers 11 Codes for conversion elasticities Float 12 Iteration variables Float 13 Start and end year of simulation Integers 14 Number and coding of explanatory factors that change Integers every year 15 Output file choice 1 0 2 or 2 16 Region specific regression choice 0 1 or 2 17 Initialization of land use history 0 1 or 2 gt Run the CLUE S model with the new settings for the period 1989 1996 Use the regon fil and demand in3 for the simulation Import the result cov_all 7 to ArcView and compare it with the actual land use of 1996 cov1996 asc When you are not satisfied with the result you can change one or more of the parameters and do the simulation again You might even create and add a new driving factor Exercise CLUE 8 Validation In exercise CLUE 7 you compared visually the result of the simulation with the real land use pattern in 1996 This is a rather subjective manner of validation No generally agreed upon method for the quantitative evaluation of the goodness of fit for simulation models has evolved yet However in this exercise you will use a more objective m
14. Practical Explorative modeling of future land use for the Randstad region of the Netherlands Peter H Verburg and Jan Peter Lesschen See eee Wageningen University the Netherlands Tg Data and CLUE software self extracting installation cluesmfl exe Software Arcview Spatial Analyst Extension SPSS Introduction The Randstad and the Green Heart Randstad Holland is a name with no city It has no official status no mayor and no municipal council Nor is there any organisation especially responsible for land use planning in the Randstad No one knows exactly where the Randstad begins and ends and there are no conclusive figures on either its surface area or population The term Randstad Holland was launched in the 1930s to denote a group of towns and cities located relatively close together in the west of the Netherlands The name is thus considerably younger than the towns and cities it stands for These are arranged roughly in the form of a horseshoe with a diameter of some 50 to 60 kilometres A few small towns and numerous villages are located in the open space in between This is largely a rural area with space for livestock and arable farming market gardening and leisure pursuits And it is this that gives the Randstad a totally different character than for example London or Paris The urban areas are grouped around a rural heart the Green Heart of the Randstad In the past few years more and more studies have been using the
15. ameters to change are the conversion elasticities Table 6 2 The CLUE S model is quite sensitive for these conversion elasticities The conversion elasticity is specified in the Main Parameters input file that can be edited through the user interface The conversion elasticity of each land use type is specified on line 11 of this file The first conversion elasticity corresponds with land use type 0 the second with land use type 1 etc Table 7 2 Current settings of the conversion elasticities Code Land use type Conversion elasticity 0 Other agriculture 0 6 1 Grassland 0 2 2 Arable land 0 2 3 Greenhouses 0 9 4 Residential area 1 5 Industrial commercial 1 6 Forest nature 0 8 7 Recreation 0 8 8 Airports 1 gt Edit the conversion elasticities for the simulation of land use change between 1989 and 1996 As last you have to change some settings in the main parameter file besides the conversion elasticities Each line of the main parameter file describes one or more parameters of the model Table 7 3 Due to the changes in the regression equations the maximum number of independent variables in a regression equation line 3 maybe has to be adapted Also the end year of the simulation line 13 should be adapted The other parameters can remain the same Only you should change the number of driving factors line 4 when you add an extra driving factor Table 7 3 Main parameters see manual for further explanation
16. anner according to a multiple resolution procedure for the model goodness of fit This method has been developed by Costanza 1989 It is based on the measuring of similarity of the patterns and the idea that measurement at one resolution is not sufficient to describe complex patterns The method yields indices that summarize the way the fit changes as the resolution of measurement changes An expanding window is used to gradually degrade the resolution of the comparison With this method the near misses besides the direct hits receive some weight and tell whether the pattern was being relatively well matched The fit for each sampling window is estimated as 1 minus the proportion of cells that would have to be changed to make the sampling windows each have the same number of cells in each category regardless of their spatial arrangement A formula was developed that retains more of the information about the relative proportions of categories within the sample window in estimating fit while decreasing the resolution Pp ty dle Tai i l a s l 2w t w F is the fit for sampling window size w w the dimension of one side of the square sampling window agi the number of cells of category 7 in scene amp in the sampling window p the number of different categories i e land use types in the sampling windows s the sampling window of dimension w by w which slides through the scene one cell at a time and 4 the total number of
17. ation variable JTER for all land use types by allocating the land use type with the highest total probability for the considered grid cell Conversions that are not allowed according to the conversion matrix are not allocated This allocation process will cause a certain number of grid cells to change land use The total allocated area of each land use is now compared to the land use requirements demand For land use types where the allocated area is smaller than the demanded area the value of the iteration variable is increased For land use types for which too much is allocated the value is decreased Through this procedure it is possible that the local suitability based on the location factors is overruled by the iteration variable due to the differences in regional demand The procedure followed balances the bottom up allocation based on location suitability and the top down allocation based on regional demand Steps 2 to 4 are repeated as long as the demands are not correctly allocated When allocation equals demand the final map is saved and the calculations can continue for the next time step Some of the allocated changes are irreversible while others are dependent on the changes in earlier time steps Therefore the simulations tend to result in complex non linear changes in land use pattern characteristic for complex systems PE T too mance colon CLUE s allocation procedure Conversion elasticity Competative Allowed strength co
18. c regression you can create probability maps based on the regression equations Use the Calculate probability maps button in CLUE S under Mode and click Run CLUE S Note you have to select an area restriction and demand scenario file although these do not influence the probability map The probability map for each land use type is stored in prob1_ 1 where indicates the land use code These maps can be imported as floating point grids in ArcView File Import Data Source ASCII Raster Note do not import as integers 7 5 Change other input data and settings The parameters for the regression results have been imported but other parameters still have to be adapted The conversion matrix the conversion elasticities and the main parameters will be adapted according to your own settings The current conversion matrix is rather simple and some unlikely land use changes are still possible 1 1 1 47 ad 4 4 141 Code Land use type 1 1 1 1 1 1 4 141 0 Other agriculture tiilalalalialialiala 1 Grassland T t 0 tet lal a4 _ reenhouses cl el ce Be 4 Residential area O19 0 4 1 17 9 1 1 5 Industrial commercial 1 1 1 1 1 1 1j1 1 6 Forest nature 1 11 1111 1j1 7 Recreation 0 0 0 1 111 0 111 8 Airports gt Edit the conversion matrix as such that it will be more realistic for the simulation of land use change between 1989 and 1996 The next par
19. c7 4 Dyna CLUE Edit input Mode File Check Help Demand scenario demand in1 1 762 region_ehs fil demand in2 fegion_green fil demand in3 0 000197 0 0 185 5 0 0155 6 3 063 0 00133 0 0 669 2 Run CLUE S OAA NEER wd Iteration Figure c7 4 Editor for the regression results This file is structures as follows Figure c7 5 Line 1 Number code for land use type e g grassland Line 2 Constant of regression equation for land use type Bo Line 3 Number of explanatory factors sclgr fil files in the regression equation for that land use type Line 4 and further On each line the beta coefficients 81 B2 etc for the explanatory factor and the number code of the explanatory factor Number code B alloct reg Notepad BEES of land use ed ED Rm Soe Ee type euete Constant 1 1738 0 Number of 30 0529 1 explanatory pahuus Number code of factors explanatory factor 2 9433 e g population 1 2045 0 0 0177 1 0 1399 2 Beta value for explanatory 30 9688 factor T 3732 0 0 4147 1 13 1919 2 Figure c7 5 Structure of the regression results file aXoc1 reg gt Use the output of your regression analysis to change the input file for CLUE S Use the coding system of the land use types and location factors similar to the coding used in Table 7 1 and the filenames sc7gr fil where is the code for the driving factor Save your changes To visualize the result of the logisti
20. ct the observed land use as State variable with value 1 for occurrence of this land use type and the Predicted probability as Test variable Click OK Figure c7 2 e The results will show a ROC curve and the area under the curve that is the test result Figure c7 3 When the ROC value is very low e g below 0 700 you should reconsider your driving factors and do the logistic regression again with other driving factors When you are satisfied with the ROC value write down the resulting values for the driving factors and the constant Bo Li jj x covd Test Variable D cov D Predicted probability p cov3 covd cov5 cov6 cov cov elev groundw om_sub D clay_sub ca_sub D popdens dis_town dis_stat dis_wate x Paste Reset State Variable Cancel Ld cov2 Help Value of State Variable fi Display IV ROC Curve IV With diagonal reference line FREE J Standard error and confidence interval I Coordinate points of the ROC Curve i Options Figure c7 2 Window for ROC curve configuration f Output SPSS Viewer Viewer File Edit View Insert Format Analyze Graphs Utilities Window Help galsi amp 3 Bele a E AE lEn Block 1 Method Forward Step hl ROC Curve LE Omnibus Tests of Model Coet LE Model Summary LE Classification Table L Variables in the Equation LE Mode
21. dicates which cells of a rectangular grid are part of the case study area and can also contain information on the locations that belong to an area with restrictions to land use conversion e g a natural park Select demand in1 as land requirements demand input file The land requirements file contains for each year that is simulated the required area of the different land use types These claims can be calculated in other models or can be based on trend extrapolation and demographic projections Different land requirements are possible for different scenarios e Click Run CLUE S The simulations will now start and the status bars show the progress Figure c1 3 NOTE The status bar for the iterative procedure shows the average difference between the allocated area of the different land use types and the required allocation of the different land use types The simulation of one year is finished if the allocated area deviates less than the specified maximum allowed deviation Only when one of the land use types exceeds the specified maximum deviation between allocation and requirements for one of the land use types the iterations will continue and a special indicator will appear on the screen CLUE S v2 4 Edit input File Check Help Average difference between allocated area and required allocation demand Figure c1 3 Explanation of the CLUE S model run When all simulations are made successfully the model wi
22. ecify a name and directory where you want to store the resulting grid e g year19 The program will prompt Cell values as integers Click Yeg The program will prompt Add grid as theme to the View Click Yes The result of the simulation can now be seen on the screen and analysed using ArcView Figure c1 4 IT sox Eile Edit Yiew Theme Analysis Surface A Window Help E Ae ERIK 2 SE Ey a Open Print Figure c1 4 Simulation result displayed in ArcView It is now possible to change the graphical presentation by changing the colours of the map into colour that are easily associated with the different land use type For the simulation of The Randstad the suggested colours of Table 1 1 can be used Table 1 1 Land use types with suggested colours Land use code Land use type Suggested colour 0 Other agriculture Yellow 1 Grassland Light green 2 Arable land Brown 3 Greenhouses Blue green 4 Residential area Red 5 Industrial commercial Purple 6 Forest nature Dark green 7 Recreation Orange 8 Airport Black gt Prepare and compare maps of the results of the simulation for 1989 2000 and 2008 Exercise CLUE 2 Testing model options 2 1 Scenario conditions The CLUE S model has a number of parameters that need to be specified before a simulation can be made The setting of these parameters depends on the assumptions made for a particular scenario In the next exercises we will
23. er These differences in behaviour towards conversion of the different land use types can be approximated by the conversion costs However costs cannot represent all factors that influence the decisions towards conversion such as nutrient depletion esthetical value etc Therefore in the model we have assigned each land use type a dimensionless factor that represents the relative elasticity to conversion ranging from 0 easy conversion to 1 irreversible change The user should specify this factor based on expert knowledge or observed behaviour in the recent past An extended explanation of the possible values of the conversion elasticity and how behaviour changes when the land requirements increase or decrease in time is given below 0 Means that all changes for that land use type are allowed independent from the current land use of a location This means that a certain land use type can be removed at one place and allocated at another place at the same time e g shifting cultivation gt 0 lt 1 Means that changes are allowed however the higher the value the higher the preference that will be given to locations that are already under this land use type This setting is relevant for land use types with high conversion costs 1 Means that grid cells with one land use type can never be added and removed at the same time This is relevant for land use types that are difficult to convert e g urban settlements and primary forests A value
24. euten and De Meern to the west of Utrecht From approximately 1990 the government working on the basis of the Fourth Policy Document VINEX has attempted to curb these developments The Green Heart has been given official boundaries and it is now regarded as part of Randstad Holland Randstad Holland thus consists of a ring of cities with a population of some 6 million and a central area with some 670 000 inhabitants Population density in the ring is approximately 1 680 per km2 and in the Green Heart 470 per km2 ue slightly higher than in the Netherlands as a whole The largest part of the Green Heart is located in the province of South Holland with smaller segments in North Holland and Utrecht The Green Heart contains 43 entire municipalities and parts of 27 more Six Green Heart municipalities may be regarded as largely urban i e Gouda Alphen aan de Rijn each of which have a 70 000 inhabitants Woerden Waddinxveen Boskoop and De Ronde Venen which includes Mijdrecht The Green Heart is thus not entirely rural nor is the ring of cities entirely urban with more or less green belts between these cities The population is still growing more rapidly in the Green Heart than in the Netherlands as a whole though the difference is by no means as great as in the 1970s A graph shows that while population growth in the Green Heart s rural municipalities has been slightly lower than the national average since the late 1980s the populations of t
25. explore four different scenario conditions 1 Land requirements 2 Spatial policies area restrictions 3 Conversion elasticity 4 Land use conversion sequences Different scenarios allow the comparison of different possible developments and give insight in the functioning of the model Such analysis is most easy by visual comparison or through the calculation of the differences between the two scenarios in a GIS gt Run the model with the baseline scenario use the original settings of the main parameters regression results and conversion matrix select region fil and demand in1 Import the results e g for the start and end of the simulation year 0 and year 19 and compare them in ArcView ArcView maps can be exported to other graphical formats by File Export Select the Windows Bitmap bmp format for the best results 2 2 Land requirements The land requirements are input to the model For each year of the simulation these requirements determine the total area of each land use type that needs to be allocated by the model The iterative procedure will ensure that the difference between allocated land cover and the land requirements is minimized Land requirements are calculated independently from the CLUE S model itself which calculates the spatial allocation of land use change only The calculation of the land use requirements can be based on a range of methods depending on the case study and the scenario The
26. extrapolation of trends of land use change of the recent past into the near future is a common technique to calculate the land use requirements When necessary these trends can be corrected for changes in population growth and or diminishing land resources For policy analysis it is also possible to base the land use requirements on advanced models of macro economic changes which can serve to provide scenario conditions that relate policy targets to land use change requirements Two different files with land requirements are provided with the model for the period from 1989 to 2008 The first scenario file demand in1 is based on the assumption of moderate urbanisation for the period 1989 till 2008 An increase in the demand of 1 5 per year is expected for the land use types greenhouses residential areas industrial commercial recreation and airports The demand for the land use types arable land and forest nature will remain constant and a decrease is expected for other agriculture and grassland The second scenario file demand in2 is based on the assumption of a very strong urbanisation in the period 1989 till 2008 The demand for the land use types residential area industrial commercial recreation and airports increases strongly with 3 per year This results in a decrease of the land use types other agriculture grassland and arable land The demand for the land use types nature forest and greenhouses is expected to remain equal gt Selec
27. he installation directory The rows of this matrix indicate the land use types during time step and the columns indicate the land use types in time step 7 If the value of a cell is 1 the conversion is allowed while a 0 indicates that the conversion is not possible The rows and columns follow the number code of the land use types Example in the matrix below all conversion are possible except the conversion from residential area industrial commercial and airports into other agriculture grassland arable land or forest nature S S13 2 2 sl 4 Code Land use type 2 E E E 3 0 Other agriculture S 8 35 g 1 Grassland X 2 Arable land Other agriculture 1 11 1111 1 1 HoA Grassland 111111111 5 Industrial commercial Arable land 1 1 1 1 1 1 1 1 1 6 Forest nature Greenhouses 1 1 1 1 1 1 1 1 1 7 Recreation Residential area olololililil olila 8 Airports Industrial O OF Of 1 1 1 OF 1 1 Forest nature 1 1 1 1 1 1 1 1 1 Recreation 1 1 1 1 1 1 1 1 1 Airports 0 0 Of 1 1 1 OF 1 1 gt Run the baseline scenario for the Randstad with a different setting of the conversion matrix keeping all other settings equal and analyse the differences in outcome with ArcView Compare the results Note Some land use conversion settings will have no effect because they are overruled by the conversion elasticity and land requirement se
28. he maps Is the designation of EHS areas necessary for concentrated nature development Exercise CLUE 7 Creating a new application In the previous exercises you explored the CLUE S model and learned how to adapt the different input files and scenarios The results of the simulation were not very realistic because the model has not been validated The conversion matrix the conversion elasticities and the regression results are all rough estimations In this exercise you will create yourself a new application for the Randstad for simulation of the period 1989 till 1996 A second land use map for 1996 is available This gives the opportunity to validate the model that you have created which will be done in Exercise CLUE 8 7 1 Selection of location factors One of the most important input files is the regression result This file contains the parameters of the logistic regression for each land use type The CLUE S model uses a statistical analysis to define the suitability of locations for different land use types The suitability of a location is a function of a number of case study specific location factors such as soil quality accessibility socio economic conditions etc For this exercise you will have to make a statistical analysis to relate these location factors to the suitability of the different land use types by a logit model The driving factors can be positive related with the land use type e g population density with residential areas
29. he outcome of the interaction between the different actors and decision making processes that have resulted in a spatial land use configuration The preference of a location is empirically estimated from a set of factors that are based on the different disciplinary understandings of the determinants of land use change The preference is calculated following Re aeX1i bX nnn where R is the preference to devote location 7 to land use type k X7 2 ate biophysical or socio economical characteristics of location 7 and ag and bg the relative impact of these characteristics on the preference for land use type amp The exact specification of the model should be based on a thorough review of the processes important to the spatial allocation of land use in the studied region A statistical model can be developed as a binomial logit model of two choices convert location 7 into land use type amp or not The preference Rw is assumed to be the underlying response of this choice However the preference Ry cannot be observed or measured directly and has therefore to be calculated has a probability The function that relates these probabilities with the biophysical and socio economic location characteristics is defined in a logit model following P Log 1 P Po PiX ii BX aie BX where P is the probability of a grid cell for the occurrence of the considered land use type on location 7 and the X s are the location factors The coefficie
30. he urban municipalities have been growing twice as rapidly as those in the rest of the country Protection is no longer the sole objective of land use planning policy for the Green Heart which is now drawn up in close consultation with local authorities and other organisations Apart from restrictive measures in relation for example to businesses and new housing policy largely focuses on developing the Green Heart s potential Importantly the area is not regarded as a single entity Traditionally settlements were built alongside rivers such as the Oude Rijn the Hollandse IJssel and the Gouwe where the land was higher and drier than the surrounding marshes These settlements formed a basis from which marsh could be reclaimed Nowadays the Green Heart can be divided into a number of segments on the basis of these more densely populated zones There are roughly three main areas fens and grassland to the north interspersed with lakes and ponds very open fens and grassland on either side of the Lek river to the south and a mixed area grassland arable farms and market gardens to the west Policy needs to be adapted to these regional differences In the residential areas the main objective is to curb urban sprawl and the further development of infrastructure the keynotes in the lakeland areas are nature conservation and expanding recreational facilities in the fens and grasslands ways need to be sought to combine livestock farming with na
31. i 128 213 DIS_WATE 421 829 Constant 1111 954 Bio POPDENS j 60 725 Title L amp Omnibus Tests of Model Coef LEJ Model Summary L amp Classification Table OM_SUB 138 017 DIS_WATE f T 341 712 Constant i 693 247 a Variable s entered on step 1 OM_SUB b Variable s entered on step 2 DIS_WATE c Variable s entered on step 3 POPDENS ole es in the Equation LEJ Model if Term Removed LEJ Variables not in the Equation ft La SPSS Processor is ready IH 230 7 Figure 7 1 Output section of the logistic regression with SPSS The Variable in the equation table in the Block 1 output section contains the resulting logit model Figure 7 1 The 8 values for the driving factors are the values in the column B for the last step in the example of Figure 7 1 the 8 value for population density POPDENS is 0 002 By default only 3 decimals are shown this number can be increased in the Cell properties window Select the specific cells and go to Format Cell Properties 7 3 Evaluating the goodness of fit with the ROC method The ROC characteristic is a measure for the goodness of fit of a logistic regression model similar to the R statistic in Ordinary Least Square regression Pontius and Schneider 2001 A completely random model gives a ROC value of 0 5 a perfect fit results in a ROC value of 1 0 gt Calculate the ROC value for your regression model e Click Graphs ROC Curve Sele
32. ic conversion settings land use requirements demand and location characteristics For recent developments on the CLUE s model and to download the latest version we refer to http www cluemodel nl non spatial analysis Driving factors of change Land use demand spatial analysis Driving factors Land use allocation of location Figure 1 Overview of the modelling procedure Spatial policies Land use type specific and restrictions conversion settings Nature parks HN Conversion elasticity Restricted areas Land use transition sequences Agricultural development zones CLUE s Land use change allocation procedure Land use requirements Location characteristics demand 7 specific Logistic ragression Aggregate demand advanced models Figure 2 Overview of the information flow in the CLUE S model Spatial policies and restrictions Spatial policies and land tenure can influence the pattern of land use change Spatial policies and restrictions mostly indicate areas where land use changes are restricted through policies or tenure status For the simulation maps that indicate the areas for which the spatial policy is implemented must be supplied Some spatial policies restrict all land use change in a certain area e g a log ban within a forest reserve Other land use policies restrict a set of specific land use conversions e g residential construction in designated agricultural areas or permanent agricul
33. idential area and social cultural facilities incl houses roads within residential areas schools hospitals churches etc Mining areas industries harbours shopping malls prisons and all service industries Forests and natural areas incl peat areas swamps heather etc Parks sport fields kitchen garden complexes camp sites etc Airports fil where is the code of the variable Description Table 1 Code 0 1 2 3 4 5 6 7 8 Table 2 Code 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Population density Biophysical variables Elevation Groundwater level Distance to water Soil Characteristics Organic matter content Calcium content Clay content Socio economic variables Travel time to station Travel time to airport Travel time to harbour Travel time to work Travel time to highway Travel time to city Travel time to town Travel time to recreation Population density person km in 1989 Mean elevation calculated from a high resolution Digital Elevation Model Groundwater dynamics characterized by depth and fluctuation through the year higher categories are better drained soils Distance to the coast or main river no network Properties of soils based on the 1 50 000 soil map Organic matter content of the subsoil 35 125 cm Calcium content of the subsoil 35 125 cm Clay content of the subsoil 35 125 cm Travel time to nearest railway station Travel time to Schiphol airport
34. ith explorer and double click cues exe The user interface should appear on the screen Figure c1 1 Dyna CLUE Edit input Mode File Check Help tegion_ehs fil region_green fil Run CLUE S E EA AAE ai Iteration Figure c1 1 Interface of the CLUE S model 1 2 Main functions The user interface makes it possible to edit the main input files through a built in text editor and allows the user to choose the scenario conditions When all parameters are set the simulation can start by clicking the Run CLUE S button Simulation results will be saved to output files that can be imported by a GIS for display and analysis Main parameters Edit the main setting of the model file main 1 Regression results Edit the regression equations file alloc1 reg Change matrix Edit land use conversion matrix file allow txt Neighborhood settings Edit neighborhood settings file neighmat txt Neighborhood results Edit neighborhood results file alloc2 reg Dyna CLUE Edit input Mode File Check Help Selection of area sori fi demendint Selection of land restrictions input slip Laan requirements file fegion_green fil lemand in input file Run CLUE S Start the simulations Figure c1 2 Explanation of the CLUE S interface 1 3 Start the simulation Make sure that all input files are correctly defined e Select region fil as area restriction input file The area restriction file in
35. l if Term Removed LE Variables not in the Equation E Logistic Regression Title i Notes La Case Processing Summary La Dependent Variable Encoding iE Block 0 Beginning Block Title LE Classification Table LE Variables in the Equation LE Variables not in the Equation are Biok 1 Method Forward Steph 0 00 25 Title LEJ Omnibus Tests of Model Coef pS LEJ Model Summary 1 Specificity LE Classification Table LE Variables in the Equation LEJ Model if Term Removed Li Variables not in the Equation E ROC Curve Title Notes Test Result Variable s Predicted probability La Case Processing Summary iin Roc curve LE Area Under the Curve Sensitivity Diagonal segments are produced by ties Area Under the Curve The leolicoullyaiable s Picdicled piubabilily liao al Figure c7 3 ROC curve of a logistic regression with SPSS 7 4 Using the regression results as input to the CLUE S model The regression results are used by the CLUE S model to determine the suitability of the locations for the different land use types For each land use type considered a separate regression model is used The result of the analysis in SPSS should therefore be translated to input for the CLUE S model The input file for the regression equations is called a ocT reg and is located in the installation directory and can be edited through the user interface of CLUE S Figure
36. ll display the message finished and a button that gives access to the LOG file will appear The log file contains information on the input files and run time information on the iterations and may be consulted when errors occur or unexpected results are found 1 4 Display of the simulation results All results of the simulation are saved in the installation directory To display the simulation results it is needed to use a GIS package For these exercises we will use the ArcView package with the Spatial Analyst Extension Follow the steps below to display a land use map generated by the CLUE S model e Start ArcView and make sure that the Spatial Analyst extension is installed and activated File Extensions Check Spatial Analyst OK Open a new View by selecting Views from the project window and click the New button Import the file with simulation results that you want to display File Import Data Source Select import file type ASCII Raster OK e Select the file with simulation results Go to the installation directory of CLUE S Note the installation directory should not contain spaces or special characters since this might disturb the import of ASCII files in ArcView Set List Files of Type at All Files The simulation results are stored in files called cov_a l where indicates the year after the start of the simulation Select the file you want e g cov_a l 19 and click OK Sp
37. low a recovery of the soll before the foreet conversion allowed location can be reclamed for agriculture again A Figure 4 Example of a land use conversion matrix with the different options implemented in the model Land use requirements demand Land use requirements demand are calculated at the aggregate level the level of the case study as a whole as part of a specific scenario The land use requirements constrain the simulation by defining the totally required change in land use All changes in individual pixels should add up to these requirements In the approach land use requirements are calculated independently from the CLUE S model itself The calculation of these land use requirements is based on a range of methods depending on the case study and the scenario The extrapolation of trends in land use change of the recent past into the near future is a common technique to calculate land use requirements When necessaty these trends can be corrected for changes in population growth and or diminishing land resources For policy analysis it is also possible to base land use requirements on advanced models of macro economic changes which can serve to provide scenario conditions that relate policy targets to land use change requirements Location characteristics Land use conversions ate expected to take place at locations with the highest preference for the specific type of land use at that moment in time Preference represents t
38. m and maximum number of years before a conversion can or should happen is indicated in the conversion table The exact number of years depends on the land use pressure and location specific conditions The simulation of these interactions combined with the constraints set in the conversion matrix will determine the length of the period before a conversion occurs Figure 4 provides an example of the use of a conversion matrix for a simplified situation with only three land use types Land use change sequence Land use conversion matrix land use b logging griculture S present land use lt a cultural sion n c abandonment forest gt agriculture grassland Forest ec omes ool conversion possible conversion not possible Figure 3 Illustration of the translation of a hypothetical land use change sequence into a land use conversion matrix Land use conversion matrix 8 EE s 38 The location can be cultivated for Max maximum three years on a row land j Sys Soll nutrient conditions do not Min Min allow a longer cultivation period aye 6 a Regrowth 4 protected area forast forest conversion not allowed conversion possible conversion not possible Only if a plece of land is tallow for 5 years on a row regrowth of tree species might be sufficient to classify it as regrowth forest The fallow period should at least have a duration of three years to al
39. mates the regionally population pressure for the location instead of only representing the population living at the location itself Allocation procedure When all input is provided the CLUE S model calculates with discrete time steps the most likely changes in land use given the before described restrictions and suitabilities The allocation procedure is summarized in Figure 5 The following steps are taken to allocate the changes in land use 1 The first step includes the determination of all grid cells that are allowed to change Grid cells that are either part of a protected area or presently under a land use type that is not allowed to change are excluded from further calculation Also the locations where certain conversions are not allowed due to the specification of the conversion matrix are identified Por each grid cell 7 the total probability TPROPz is calculated for each of the land use types according to TPROP P ELAS ITER where Piu is the suitability of location for land use type w based on the logit model ELASw is the conversion elasticity for land use v and ITER is an iteration variable that is specific to the land use type and indicative for the relative competitive strength of the land use type ELAS the land use type specific elasticity to change value is only added if grid cell 7 is already under land use type z in the year considered A preliminary allocation is made with an equal value of the iter
40. nd use map cov_a 0 Copy the results to Excel and plot the data together with calculated fit for the predicted land use map Calculate also the F value for the initial land use map What is your conclusion does your model have any additional value gt Compare the validation results with other students Discuss the outcomes and try to improve the model by adapting the regression results driving factors conversion matrix and or conversion elasticities Then run the CLUE S model again and calculate the new fit Exercise CLUE 9 Create your own scenario Write a storyline of a scenario for the development of the Randstad region for the period 1996 most recent data to 2016 The storyline should include the background of the expected developments in the region e g large urbanization is expected because Netherlands has become the Capital of the United States of Europe The storyline should contain aspects that describe the quantity of change and regulations and policies that determine the location and nature of change Translate your storyline to model conditions including the demand for the different land use types restricted areas elasticities to change e g policies that restrict the re location of certain land use types conversion settings including spatial policies aimed at certain conversions gt Use the regression equations your validated model exercise CLUE 8 to run the model for 20 years gt You must reconfig
41. nts f are estimated through logistic regression using the actual land use pattern as dependent variable This method is similar to econometric analysis of land use change which is very common in deforestation studies In econometric studies the assumed behaviour is profit maximization which limits the location characteristics to agricultural economic factors In the study areas is assumed that locations are devoted to the land use type with the highest suitability Suitability includes the monetary profit but can also include cultural and other factors that lead to deviations from economic rational behaviour in land allocation This assumption makes it possible to include a wide variety of location characteristics or their proxies to estimate the logit function that defines the relative probabilities for the different land use types Most of these location characteristics relate to the location directly such as soil characteristics and altitude However land management decisions for a certain location are not always based on location specific characteristics alone Conditions at other levels e g the household community or administrative level can influence the decisions as well These factors are represented by accessibility measures indicating the position of the location relative to important regional facilities such as the market and by the use of spatially lagged variables A spatially lagged measure of the population density approxi
42. nversions ELASy ITERu Calculation of Is the total by Land use t land use area equal Land use t 1 to the demand a Grid cell specific settings Location suitability Piu Spatial Regional policies demand Figure 5 Flow chart of the allocation module of the CLUE S model Data description of case study In these exercises the CLUE S model will be used for the simulation of land use change for the Randstad in The Netherlands The selected region comprises the area between the four main cities Amsterdam Den Haag Rotterdam and Utrecht Figure 6 Nine different land uses have been distinguished in the area Table 1 All No Data area is water but is not taken into account Table 2 lists the available driving factors for land use change Land use 1989 C Other agriculture E Grassland Gi Arable land i Greenhouses Gi Residential area GR In dustrial commercial Gi Forest nature yj Recreation Be Airports C No Data Figure 6 Initial land use map 1989 Land use types file cov_all 0 Name Other agriculture Grassland Arable land Greenhouses Residential area Industrial commercial Forest nature Recreation Airports Driving factors file sclgr Variable Description Agricultural land not belonging to grassland arable land or greenhouses This class includes horticulture orchards bulbs etc Grasslands incl semi natural grasslands All arable lands Greenhouses Res
43. o 1 2 3 0 6 0 2 0 2 0 9 Run CLUE S e Click on Save e Run the model after selecting the Area restrictions file and the Land requirements file for the base scenario e Display the results with ArcView Compare the differences in spatial pattern of land use change as result of the changes in conversion elasticity Try to explain why your changes resulted in a different land use pattern NOTE Each simulation the model will overwrite the results of a previous simulation If you want to save the results rename the output files or move the output files to another directory Exercise CLUE 5 Land use conversion sequences Not all land use changes are possible and some land use changes are very unlikely e g arable land cannot be converted into primary rain forest Many land use conversions follow a certain sequence or cycle e g fallow land and forest regrowth often follow shifting cultivation The conversions that are possible and impossible are specified in a land use conversion matrix For each land use type it is indicated in what other land use types it can be converted during the next time step Depending on the definition of this conversion matrix and the time steps chosen complex land use sequences are possible The land use conversion matrix can be edited by clicking Change matrix under Edit Input It is also possible to use a text editor e g Notepad to edit the file aXow txt in t
44. of one stabilizes the system and prevents that in case of deforestation other areas are reforested at the same time The conversion elasticity is specified in the Main Parameters input file that can be edited through the user interface click Main Parameters in the Edit Input menu or with a text editor in the file main 1 in the installation directory The conversion elasticity of each land use type is specified on line 11 of this file an explanation of all other parameters in this file can be found in the user manual The first conversion elasticity corresponds with land use type 0 the second with land use type 1 etc Table 4 1 Current settings of the conversion elasticities Code Land use type Conversion elasticity 0 Other agriculture 0 6 1 Grassland 0 2 2 Arable land 0 2 3 Greenhouses 0 9 4 Residential area 1 5 Industrial commercial 1 6 Forest nature 0 8 7 Recreation 0 8 8 Airports 1 gt Run the CLUE S model with alternative settings for the conversion elasticity Change the conversion elasticity by e Click on Main Parameters under Edit Input The main parameters can now be edited e Line 11 contains the conversion elasticity settings of the different land use types in the same order as the land use type coding Change these values to new values Dyna CLUE Edit input Mode File Check Help demand int demand in2 region_green fil demand in3 70197 662288048 434782 50168974
45. ructure The model is sub divided into two distinct modules namely a non spatial demand module and a spatially explicit allocation procedure Figure 1 The non spatial module calculates the area change for all land use types at the aggregate level Within the second part of the model these demands are translated into land use changes at different locations within the study region using a raster based system The user interface of the CLUE S model only supports the spatial allocation of land use change For the land use demand module different model specifications are possible ranging from simple trend extrapolations to complex economic models The choice for a specific model is very much dependent on the nature of the most important land use conversions taking place within the study area and the scenarios that need to be considered The results from the demand module need to specify on a yearly basis the area covered by the different land use types which is a direct input for the allocation module The allocation is based upon a combination of empirical spatial analysis and dynamic modelling Figure 2 gives an overview of the information needed to run the CLUE S model This information is subdivided into four categories that together create a set of conditions and possibilities for which the model calculates the best solution in an iterative procedure The next sections discuss each of the boxes spatial policies and restrictions land use type specif
46. s or one of the other methods for stepwise regression Select for this exercise the Forward Conditional method xi Dependent La 2 Don Paste Block 1 of 1 Next Reset Cancel Covariates Help FRR dis_t gt of xl Method Forward Conditional Select gt gt Categorical Save Options e Click the Save button and check the probabilities option to save the predicted probabilities e Click the Options button to set the conditions for stepwise regression In the probability for stepwise section you can indicate the significance values for Entry and Removal during the stepwise procedure For large data sets these values can be set at respectively 0 01 and 0 02 e Run the regression by clicking OK e Output will be generated in a special window Use the help function if you have problems interpreting the output z Outputi SPSS Viewer File Edit View Insert Format Analyze Graphs Utilities Window Help slala Go 3 Elle a of l cio ziale E Output amp Logistic Regression Title A Notes La Case Processing Summary LEJ Dependent Variable Encoding amp Block 0 Beginning Block Title LEJ Classification Table LEJ Variables in the Equation La Variables not in the Equation E amp Block 1 Method Forward Step Variables in the Equation OM_SUB 132 483 Constant i 1912 748 OM_SUB
47. s scenario is based on the real land use changes between 1989 and 1996 A linear increase or decrease of the demand is supposed for each land use type during this period The time span between the two years is short for a validation but no other comparable data set is yet available Three area restriction files are available The default file is region fil which indicates all land areas for which land use change calculations should be made The second file region_ehs fil is based on the default file but also indicates the areas that belong to the ecological main structure EHS of The Netherlands Land use changes are not allowed in these areas The third area restriction file is region_green fil this file indicates The Green Heart the open space with many grasslands in between the four major cities No land use changes are allowed in this area if this file is chosen Please zoze that not all combinations of area restriction file and demand files are possible e g a strong urban expansion while protecting a large part of the landscape will not yield a solution Exercise CLUE 1 Getting to know the user interface This exercise makes you familiar with the user interface of CLUE S The precise definition of the different parameters and input files is discussed in other exercises and in the user manual Download all data to directory d prac_clue 1 1 Start CLUE S CLUE S can be started by Opening the directory where CLUE S is installed w
48. t the land requirement scenario demand in2 and run the model keeping all other settings equal to the first run of the model Analyse the results with ArcView through displaying the land use pattern at the start of the simulation and at the end of the simulation NOTE Each simulation the model will overwrite the results of a previous simulation If you want to save the results rename the output files or move the output files to another directory 2 3 Define a demand scenario The two previous demand scenarios ate not very sophisticated because they are just linear trends based on an urbanisation rate In the introduction text of this chapter and in the summary of the fifth national policy document on spatial planning of the Dutch government Ministry of VROM 2002 policies for spatial planning are mentioned Use this information and other information from websites for suggestions see the reference list to compose a more realistic and sophisticated land requirement scenatio gt Define your own scenario for the period 1989 2008 by generating a new land requirements input file for CLUE S You should be able to give reasons for your choices for each land use type Follow the steps below to create your scenario e Open Microsoft Excel to facilitate the calculations You can start to make your changes based on one of the pre defined demand files e g demand in1 e Specify for each year 1989 2008 the land requirements of the different land use t
49. t up in October 1996 Though this body does not possess local government status it is the vehicle through which many parties can make their voices heard in both studies and consultations And it is the future of the Green Heart that is now under discussion A Randstad planning Dutch planners have often been admired for developing a conurbation in the form of a number of separate cities grouped around a more a less open central area However the praise is not entirely deserved since the structure of the Randstad was largely determined by geographical characteristics and historical accident At most the planners can be congratulated for doing their best to keep the structure more or less intact The emergence of the Randstad in its current form proceeded roughly as follows 1 Inthe Middle Ages the central area was largely an inaccessible marshland Settlements were built on higher ground along the dunes in the west Haarlem The Hague on the sandy ground in the east Hilversum Amersfoort along river banks Utrecht and Leiden on the Old Rhine Amsterdam on the Amstel Rotterdam on the Rotte Thanks to the fishing industry and shipping the settlements on the coast and the rivers developed into centres of trade and industry By the seventeenth century the west of the Netherlands Holland had reached such pre eminence that it also became the cultural scientific and administrative centre 2 The first attempts at town and country planning
50. the EHS is unrealistic because these areas are designated for nature development and change of agricultural into nature is expected The model has also the possibility to restrict only certain conversions such as residential construction This will be explained in more detail in Exercise 6 e Which land use type is most affected by the protection of the ecological main areas EX10 NOTE Each simulation the model will overwrite the results of a previous simulation If you want to save the results rename the output files or move the output files to another directory Exercise CLUE 4 Conversion elasticity The conversion elasticity is one of the land use type specific settings that determine the temporal dynamics of the simulation The conversion elasticity is related to the reversibility of land use changes Land use types with high capital investment or irreversible impact on the environment will not easily be converted in other uses as long as there are land requirements for those land use types Such land use types are therefore more static than other land use types Examples of relatively static land use types are residential areas but also plantations with permanent crops e g fruit trees Other land use types are more easily converted when the location becomes more suitable for other land use types Arable land often makes place for urban development while expansion of agricultural land can occur at the same time at the forest fronti
51. tion esthetical values etc Therefore for each land use type a value needs to be specified that represents the relative elasticity to change ranging from 0 easy conversion to 1 irreversible change The user should decide on this factor based on expert knowledge or observed behaviour in the recent past The second set of land use type characteristics that needs to be specified are the land use type specific conversion settings and their temporal characteristics These settings are specified in a conversion matrix This matrix defines e To what other land use types the present land use type can be converted or not Figure 3 e In which regions a specific conversion is allowed to occur and in which regions it is not allowed e How many years or time steps the land use type at a location should remain the same before it can change into another land use type This can be relevant in case of the re growth of forest Open forest cannot change directly into closed forest However after a number of years it is possible that an undisturbed open forest will change into closed forest because of re growth e The maximum number of years that a land use type can remain the same This setting is particularly suitable for arable cropping within a shifting cultivation system In these systems the number of years a piece of land can be used is commonly limited due to soil nutrient depletion and weed infestation It is important to note that only the minimu
52. tribution and to analyse the relations between the driving factors and land use types 7 2 Performing the statistical analysis In this exercise you will import the land use maps and the driving factors into SPSS for statistical analysis A file called stat xt contains all data for land use types and driving factors in tabular format for each grid cell Normally you can create the file yourself through the file conversion tool Each column contains the values of a file first column first file according to Table 7 1 etc each row contains the values for a single grid cell for all files gt Import the s at txt data file into SPSS by starting the SPSS software and selecting File Read Text Data and select the just produced file stat xt and go through the import wizard This will import all data The names of the columns are not imported and should be specified by the user The order of the columns equals the order of the files specified in the Table 7 1 In SPSS it is possible to edit the names in the Variable View tab This will facilitate the analysis gt Perform a logistic regression analysis to test the hypotheses you have specified for the different land use types e Click Analyze Regression Binary Logistic e Select a land use type as dependent variable and the location factors you have selected as independent variable e Select a method for regression Enter if you want to include all selected variable
53. ts driving factors in combination with dynamic modelling of competition between land use types The model was developed for the national and continental level and applications for Central America Ecuador China and Java Indonesia are available For study areas with such a large extent the spatial resolution for analysis was coarse and as a result each land use is represented by assigning the relative cover of each land use type to the pixels Land use data for study areas with a relatively small spatial extent is often based on land use maps or remote sensing images that denote land use types respectively by homogeneous polygons or classified pixels This results in only one dominant land use type occupying one unit of analysis Because of the differences in data representation and other features that are typical for regional applications the CLUE model cannot directly be applied at the regional scale Therefore the modelling approach has been modified and is now called CLUE S the Conversion of Land Use and its Effects at Small regional extent CLUE S is specifically developed for the spatially explicit simulation of land use change based on an empirical analysis of location suitability combined with the dynamic simulation of competition and interactions between the spatial and temporal dynamics of land use systems More information on the development of the CLUE S model can be found in Verburg et al 2002 and Verburg and Veldkamp 2003 Model st
54. ttings Each simulation the model will overwrite the results of a previous simulation If you want to save the results rename the output files or move the output files to another directory Exercise CLUE 6 Area restricted land use conversions Certain land use conversions are only allowed in specific areas due to policy constraints e g the conversion of agricultural land into greenhouses is only allowed in The Westland The CLUE S model has an option in the conversion matrix to include these area specific restrictions A map indicates where the changes are allowed The map should be named se gr fi with denoting a unique number which follow the highest of the driving factor files An example LUO LU1 LU2 Luo 1 1 20 LUI 1 1 1 LU2 1 1 In this example the conversion of land use type 0 into land use type 2 is only allowed in areas indicated in file sc gr20 fil This file has a similar format as the area restriction file with a value 1 in areas where this conversion is allowed and a value 0 for areas where this change is not allowed Note the number of driving factor files line 4 in the main parameter file ain T has to be changed according to this setting For this exercise you will simulate a policy scenario in which nature conservation and development is strongly stimulated by the government In this scenario it is assumed that the government wants to increase the nature area in the Randstad by 15 000 ha for the period 1989
55. ture and landscape conservation and in the arable farming and market gardening areas near for example Zoetermeer and Aalsmeer the emphasis is on agricultural development New housing is not wholly excluded from the picture but it may only be planned in specific locations within each municipality Up to 2005 17 000 new homes may be built in the Green Heart as opposed to 83 000 in the VINEX locations on its periphery In their policy document on land use planning and the environment the relevant provincial authorities the Ministry of Housing Spatial Planning and the Environment the Ministry of Agriculture Nature Management and Fisheries and the Ministry of Transport Public Works and Water Management have undertaken to enhance the green quality of the area To this end eleven projects involving an investment of 25 billion guilders over the next 25 years will be launched in an effort to create and link nature conservation areas develop both a waterway network for pleasure boats and a signposted cycle path network plan the transitional areas between the cities and the Green Heart and create new woodlands and recreational areas Where in the past plans and measures tended to be introduced at random an integrated approach will be adopted to these new projects C Further discussion It will be obvious from the above overview that a shift has occurred from a largely defensive approach to policies in which incentives play a key role Yet
56. ture in the buffer zone of a nature reserve The conversions that are restricted by a certain spatial policy can be indicated in a land use conversion matrix for all possible land use conversions it is indicated if the spatial policy applies Land use type specific conversion settings Land use type specific conversion settings determine the temporal dynamics of the simulations Two sets of parameters ate needed to characterize the individual land use types conversion elasticities and land use transition sequences The first parameter set the conversion elasticities is related to the reversibility of land use change Land use types with high capital investment will not easily be converted in other uses as long as there is sufficient demand Examples are residential locations but also plantations with permanent crops e g fruit trees Other land use types easily shift location when the location becomes mote suitable for other land use types Arable land often makes place for urban development while expansion of agricultural land occurs at the forest frontier An extreme example is shifting cultivation for this land use system the same location is mostly not used for petiods exceeding two seasons as a consequence of nutrient depletion of the soil These differences in behaviour towards conversion can be approximated by conversion costs However costs cannot represent all factors that influence the decisions towards conversion such as nutrient deple
57. ulation overspill At the basis of all these plans was the alarming prognosis that the Netherlands would have a population of 20 million by 2000 The current prognosis is a peak of over 17 million in 2030 followed by a gradual drop 4 In the 1970s it became evident that these plans had not been very successful either Local government authorities took little notice of them while small towns and villages were still making every effort to attract both businesses and residents As a result the Green Heart was becoming less and less green Migration from the cities to the smaller municipalities had taken on such proportions that the populations of the three major cities dropped by an average of more than 18 between 1970 and 1985 5 There was clearly a need for different town and country planning policies with central government not only setting out the main outlines but also exerting a direct influence on planning This was the principle underpinning the Third Policy Document on Town and Country Planning the first instalment of which was published in 1973 It was the second instalment the Policy Document on Urbanisation published in 1976 that formed the main breakthrough Efforts to develop locations in other parts of the country were replaced by a planning policy on the Randstad central to which was the designation of 14 towns as growth centres for new housing With three exceptions these growth centres were located outside the Randstad The
58. ure your model to start the simulation in 1996 1 delete file cov_all 0 rename file cov1996 asc into cov_all 0 2 reconfigure the demand file in such a way that it contains 21 lines one for each year of the simulation the first line containing the areas of the different land use types in 1996 calculate these based on the areas in the map cov1996 asc using ArcView taking note of the cell area of 25 hectares 3 Change the years of simulation in the main parameter file to 1996 2016 gt Run your scenario and analyze the outcome gt Prepare 1 map table diagram or graph based on your results that shows clearly the most important information relevant to policy makers that are not interested in land use modeling itself Prepare a short powerpoint presentation to present your results References of CLUE exercises Ministry of VROM 2002 Fifth national policy document on spatial planning of the Dutch government Pontius R G and Schneider L C 2001 Land use change model validation by an ROC method for the Ipswich watershed Massachusetss USA Agriculture Ecosystems and Environment 85 239 248 Veldkamp A and Fresco L O 1996 CLUE CR an integrated multi scale model to simulate land use change scenarios in Costa Rica Ecological modelling 91 231 248 Verburg P H Veldkamp A de Koning G H J Kok K and Bouma J 1999 A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use
59. were made some fifty years ago In 1958 the government published a report entitled The West of the Country in which for the first time concern was expressed at the problem of congestion in Randstad Holland The rapid growth of the economy and rising population figures led to growing fears of unbridled urban spread in the Randstad In 1960 the First Policy Document on Town and Country Planning in the Netherlands recommended reducing pressure on the Randstad by encouraging the development of peripheral areas in the north south and eastern Netherlands The Green Heart was to remain green due to its importance as an agricultural area 3 In the 1960s it became evident that this policy was meeting with very limited success The population of the Randstad continued to grow especially in the Green Heart Thanks to increasing car ownership people were becoming more and more mobile and were moving in increasing numbers to towns and villages in the green central area As employment was still only to be found in or near the cities commuter traffic grew rapidly The Second Policy Document on Town and Country Planning in the Netherlands appeared in 1966 The aim was still to deflect the growth of the cities in the Randstad to places elsewhere but now efforts centred on bordering regions such as the north of North Holland Flevoland and the Delta area Urban sprawl in the Green Heart was to be countered by reserving a limited number of locations for pop
60. will only address policies that restrict all land use changes in designated areas With this version of the model for the Randstad three area restriction files are supplied that can be selected through the user interface Each file contains a map designating the areas where land use change is restricted The maps are shown in the figures below but can also be imported in ArcView as ASCII Raster file similar to the procedure used to import the results of the simulations The files are located in the installation directory Region fil Default file that indicates all land areas for which land use change calculations should be made Region_ehs fil Area restriction file based on the default file but also indicates the areas that belong to the ecological main structure EHS of The Netherlands Land use changes are not allowed in these areas Region_green fil Area restriction file that indicates The Green Heart the open space with many grasslands in between the four major cities No land use changes are allowed in this area Ecological main structure Green heart gt Run the CLUE S model with the different area restriction files keeping all other settings equal to the first run of the model Compare the results with the initial situation 1989 year 0 and compare the impact of the different area restrictions e Is strict protection of The Green Heart needed to keep an open green area NOTE The assumption that no changes will occur in
61. ypes in a table following the specifications below Each row indicates a year each column a land use type following the order of the land use coding Make sure to include also the land requirements for 1989 year 0 These should be similar to the land use map of 1989 If you base your calculations on the number of pixels in the map you should multiply these values by 25 because the map has a resolution of 500 meter and the demand is given in hectares The total land area required should equal the area in the region fil file i e 279 625 ha The sum of the values on each row should equal this amount for each year Eile Edit View Insert Format Tools Data Window Help DSeESS2RY amp BRSlO a Sx A g 3 iy B i00 gt C T Prompt Arial 26 A3 EEA K23 v A B C D E F G H J el 0 1 2 3 4 5 6 7 8 2 1989 32425 132775 16325 10200 40875 12900 15350 16900 1875 3 1990 30889 132696 16404 10489 41382 13250 15396 17218 1900 4 1991 29354 132618 16482 10779 41889 13600 5443 17536 1925 S 1992 27818 132539 16561 11068 42396 13950 15489 17854 1950 6 1993 26282 132461 16639 11357 42904 14300 5536 18171 1975 7 1994 24746 132382 16718 11646 43411 14650 15582 18489 2000 8 1995 23211 132304 16796 11936 43918 15000 15629 18807 2025 g 1996 21675 132225 16875 12225 44425 15350 5675 19125 2050 10 1997 21458 131472 16791 12286 44958 15580 15675 19355 2

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