Nogales, Arizona and Nogales, Sonora

Simulating Urban Growth on the U.S.-Mexico Border: Nogales, Arizona and Nogales, Sonora

Soe W. Myint, Jyoti Jan Christopher Lukinbeal, Francisco Lara-Valencia 2010

Working Paper Number 25 Simulating urban growth on the U.S.-Mexico border: Nogales, Arizona and Nogales,

Sonora

(Research Paper)

(1) Soe W. Myint, (2) Jyoti Jain, (3) Christopher Lukinbeal, (4) Francisco Lara-Valencia

All authors:

School of Geographical Sciences and Urban Planning

Arizona State University

P.O. Box 875302

Tempe, AZ 85287-5302

Phone: (480) 965-7533

Fax: (480) 965-8313

Corresponding author - Email: [email protected]; Ph: (480) 965-6514; Fax: (480) 965-

8313

Abstract

The paired U.S.-Mexico border cities of Nogales, Arizona and Nogales, Sonora, known as Ambos Nogales, are the largest and most rapidly growing cities on the Arizona-Sonora border. The growing urban population is producing extensive land-use and land-cover change in the region. The continued expansion of paired cities presents many environmental management and urban planning challenges. This research employs a cellular automata model to examine the difference between the patterns and rates of urban growth and land-use change under different environmental and planning strategies in the two cities over the next 20 years (2004-2025). A series of Landsat TM images acquired over different time periods (October 1985, July 1991, February 1995, September

2000, and July 2004) were used to simulate urban growth using four planning scenarios: business as usual; environmental protection; road network; and anti-growth strategy. The study revealed that urban growth trends in the first three scenarios simulated enormous edge growth throughout the region. The anti-growth scenario, which emphasizes environmental protection, is considered the most desirable for future urban development and planning in the Ambos Nogales region.

Keywords: Nogales, planning scenarios, urban growth, Landsat

2 Introduction

Urbanization is the general process of city growth and has often been viewed as a necessary component of regional economic growth; however, unplanned and mismanaged urban growth has provoked concerns over land-use and land-cover change such as the loss of large areas of primary forest and agricultural land, inadvertent climate repercussions, and environmental degradation (Yang, 2002). Li and Yeh (2000) stated that under current growth trends the complete depletion of agricultural land resources could occur in some fast growing areas. Land-use and land-cover change is one of the most significant forms of global environmental change and is felt in both developing and developed countries (Turner et al., 1993). Furthermore, land-use change has received growing attention alongside other human-induced environmental change, such as climate and atmosphere (Walker and Steffen, 1997 & 1999; Cihlar and Jansen, 2001).

Urbanization alters the biophysical and socio-economic environment. Urban storm water runoff is a major contributing factor to water quality degradation (Driver and

Troutman, 1989; U.S. EPA, 1997). Storm water changes hydrologic patterns, accelerates natural stream flows, destroys aquatic habitat, and elevates pollutant concentrations

(Burton and Pitt, 2002). In addition, urbanization changes soil’s physical and chemical properties, water availability, vegetation, and associated animal and microbial communities (Jenerette and Wu, 2001). Urbanization poses other socio-economic challenges, such as demographic pressure, infrastructure problems, inadequate resources for service delivery, and planning (United Nations Human Settlement Programme, 2003).

The inability to effectively manage these related challenges is rapidly increasing risks associated with poor housing conditions, uncollected solid waste, over consumption of

3 limited freshwater supplies, untreated waste water, and urban air pollution (Masser,

2001). Furthermore, complex interactions among physical, biological, economic, and social forces in both the spatial and temporal domain control urban growth and sprawl

(Turner, 1987).

Cities along the U.S.-Mexico border have grown at an unprecedented pace over the last few decades. The urban growth that began with the emergence of maquila industries and passage of the North American Free Trade Agreement (NAFTA) has resulted in drastic land-use and land-cover change across the region (Esparza et al.,

2001). Land-use and land-cover change has been extensive in paired border cities which produces a plethora of urban problems and social pathologies (SCERP, 2005).

Furthermore, rapidly growing border cities are placing strains on landscape to accommodate growth (Esparza et al., 2001).

Researchers have studied various aspects of urban growth along the U.S.-Mexico border, such as air quality (US EPA, 2006), water quality (Reynolds, 2000), soil contamination, and hazardous waste (Guhathakurta et al., 2000). Social, cultural and quality of life issues along the border have also been discussed (SCERP, 2005). The paired U.S.-Mexico border cities of Nogales, Arizona and Nogales, Sonora, known as

Ambos Nogales, are the most rapidly growing cities on the Arizona-Sonora border.

Recently much research has been done on the Ambos Nogales watershed such as modeling land use change and the impact of water quality on urban growth and human health (Norman, 2005), Colonia development and settlement patterns (Norman et al.,

2006), and modeling nonpoint source pollution (Norman, 2007; Norman et al., 2008).

Continuous urban growth not only reduces the amount of open space, vegetation, and

4 forest areas but threatens the scenic, historic, and biological value of the Ambos Nogales region. Therefore, more research is needed that simulates the spatial consequences of urban growth and how it changes the landscape of U.S.-Mexico border cities.

Traditional change detection methods can only provide a static diagnosis of changes that occur during fixed periods of time. However, urban growth and sprawl is a continuous and ongoing process that requires dynamic information that often goes beyond the temporally fixed coverage of remote sensing data. The most useful information for the decision-maker is not just what and where change occurs but why such change happens, at what pace, and what will the landscape look like if the driving factors continue under normal or alternative conditions (Weng, 2002). Answers to these questions rely on an effective change process model to predict the spatial distribution of the specific land-use and land-cover classes in future years by utilizing knowledge gained from previous years. Spatial transition-based models such as the Markov chain model

(Muller and Middleton, 1994; Myint and Wang, 2006) and the Cellular Automata (CA) model (Clark, 1998) have been instrumental in predicting land-cover and land-use change.

It may not be feasible to evaluate and compare the effectiveness of numerous land change models because they are completely different in many ways. For example,

Pontius and Chen (2003) developed IDRISI’s GEOMOD that simulates change between two land categories (Pontius et al. 2001) whereas IDRISI’s cellular automata and Markov change (CA_MARKOV) simulates change among several land categories (Wagner 1997;

Wu and Webster 1998; Pontius and Malanson 2005). Other models such as the dynamic geo-referenced land use/cover model (CLUE-CR) (Veldkamp and Fresco 1996) simulates

5 change in quantitative parameters as opposed to categorical variables. Some land change models are built in raster format, while others are in vector or grid format (Pontius and

Chen, 2003). Pijanowski et al (2002) developed a land transformation model that employs artificial neural network (ANN) and GIS to predict development patterns within the six-county Grand Traverse Bay Watershed in Michigan. The study examined the relationship between several predictor variables and urbanization, and reported that the model performed with a relatively good predictive ability (46%) at a resolution of 100 m.

Silva and Clarke (2001) employed the Slope, Land Cover, Exclusion, Urbanization,

Transportation, and Hill-shade (SLEUTH) model (Clarke et al., 1997) to examine the differences in the model's behavior when different environmental variables of a European city are entered and modeled to predict future urban growth. Jantz et al (2010) developed methods that expand the capability of SLEUTH to incorporate economic, cultural, and policy information forecasts to 2030 of urban development under a current trends scenario across the entire Chesapeake Bay drainage basin. Herold et al. (2003) explored combining remote sensing, spatial metrics and spatial modeling using the SLEUTH model to analyze and model urban growth in Santa Barbara, California. It is widely accepted that there is no perfect landscape change model, but there are models that have been developed to achieve significantly different objectives (Baker, 1989; EPA, 2000;

Pontius and Chen, 2003).

The Environmental Protection Agency (2000) reviewed various landscape change models for assessing the effects of community growth and change on land-use patterns, and provides explicitly defined criteria to select the best fit land change model to project future land-use. The criteria to choose the right model include relevancy, availability of

6 resources (e.g., hardware, software), model support (e.g., model document, user discussion, training), technical expertise, data requirements, reliability and accuracy, data resolution, temporal capabilities, versatility (multiple variables), linkage potential, public accessibility, transferability, and third-party use. Considering the above criteria, we believe that the SLUETH model, a dynamic cellular automata (CA) model (Clarke et al.,

1997; Yang and Lo, 2003), is relevant and reliable, and fits well with almost all criteria listed above. Furthermore, this model has been extensively used in ―real-world‖ situations and generally recognized as an effective model by third-party users. Hence, we employed SLEUTH model to answer the following research questions: (1) What impacts will different urban growth planning scenarios have on the landscape over the next 20 years (2004-2025) in the paired U.S.-Mexico border cities of Nogales, Arizona and

Nogales, Sonora?; (2) How does the patterns and rates of urban growth and land-use change vary under these different planning scenarios?

Model Overview

The SLEUTH model is comprised of four major components: model input, parameter initialization, growth computation, and model output (Project Gigalopolis,

2001). The SLEUTH model requires six types of input data: slope, land-use and land- cover, exclusion, urban extent, transportation, and hill-shade. The land-use and land- cover theme is not required for the SLEUTH’s urban growth model. The dynamics of urban growth are expressed by four growth rules (Figure 1): (1) spontaneous growth; (2) new spreading centers (diffusive growth); (3) edge (organic) growth; and (4) road influenced growth. Spontaneous growth defines the occurrence of urban settlements

7 anywhere on a landscape (random). The second rule (new spreading centers’ diffusive growth) allows new spontaneously urbanized cells to become centers of further growth.

Organic growth, which is the most common type of development, occurs through urban infilling and along urban edges. Lastly, road influenced growth defines the occurrence of urban development along a transportation network because of increased accessibility.

These growth rules are applied to the input image data to simulate urban growth.

Five growth parameters (dispersion coefficient, breed coefficient, spread coefficient, slope coefficient, and road gravity coefficient) control how growth rules are applied. Growth parameter values are calibrated by comparing simulated land-cover change to a study area’s historical data. Once the model runs, by setting each of the coefficient values and applying growth rules to the input images, the growth rate (GR) is computed. During the urban growth computation, a second hierarchy of growth rules, called self-modification is applied if the growth rate exceeds or falls short of the limit values (Clarke and Gaydos, 1998). A ―boom‖ state occurs if GR exceeds the Critical

_high value. In a ―boom‖ state each of the coefficients is increased by a multiplier greater than one. A ―bust‖ state occurs if GR is less than the Critical _low value. In a

―bust‖ state each of the coefficients are lowered by a multiplier less than one. Both growth rules and self-modification rules are the core means by which the SLEUTH model assesses the process of urbanization; however, these rules are refined to Ambos

Nogales through the process of calibration.

The calibration process in the SLEUTH model is initialized using the earliest input data then growth cycles are generated. One growth cycle, which is the basic unit of model growth, represents one year. Control years are where historic data exists. When a

8 completed growth cycle has a corresponding control year, an image of simulated data is generated and several metrics of urban form are measured. Because each growth cycle generates a high amount of randomness, growth simulations are generated in a Monte

Carlo fashion to provide a greater amount of stability for the modeled results. The best fit values identified from calibration will be the starting values for the prediction.

Data and Study Area

A series of Landsat (TM) images at 28.50 m spatial resolution with path/row locations of 36/38 acquired over different time periods (October 1985, 1 July 1991, 2

February 1995, 3 September 2000, and 20 July 2004) were used for this study. Six channels of Landsat (TM) bands were selected for this study: blue – B1 (0.45 – 0.52 μm), green – B2 (0.52 – 0.60 μm), red – B3 (0.63 – 0.69 μm), near infrared – B4 (0.75 – 0.90

μm), mid infrared – B5 (1.55 – 1.75 μm), and mid infrared – B7 (2.09 – 2.35 μm). The thermal infrared – B6 (10.4 – 2.50 μm) was not used because of its coarse resolution. A subset of the images extent (762 by 1258 pixels), which contains the paired U.S.-Mexico border cities of Nogales, Arizona and Nogales, Sonora, was selected for this research

(Figure 2). The study area spans 77,862 ha. The area on the Arizona-side encompasses

40,912 ha whereas the area on the Sonora-side encompasses 36,950 ha. The Arizona-side covers 762 columns by 661 rows whereas the Sonora-side covers 762 columns by 597 rows in the image of the study area. All Landsat (TM) images were ortho-rectified.

Landsat TM band 4 (near infrared band), band 3 (red band), and band 2 (green band) of the study area are shown in Figure 3.

9 We employed a spatial autocorrelation approach known as the Getis index (Gi) to improve the classification accuracy of a traditional spectral-based classification approach.

We performed a supervised classification using spatial transformed bands (i.e., Getis index) to identify seven classes, namely: Urban, Agriculture and Grassland, Riparian

Vegetation, Forest, Shrubs, Exposed Soil, and Water. The forest category in this arid/semi-arid region is referred to as a desert deciduous forest and is generally composed of dwarf woody plants and small deciduous trees normally ranging between 5 meters and

10 meters above the ground with very low crown closure percent. The Gi approach with different window sizes (i.e., 3x3, 5x5, 7x7, 9x9, and 11x11) were examined over the subset of band 5, band 4, and band 3 of the Landsat image acquired in 2004. The generated Gi-transformed images with bands 5, 4, and 3 were first transformed from floating point values to unsigned 8 bit values (0 – 255). These images were then layer stacked with all the original bands. Supervised classification with parallelepiped and maximum likelihood decision rule was performed over the images generated by a combination of all the original bands and Gi-transformed bands for different window sizes (i.e., 3x3, 5x5, 7x7, 9x9, and 11x11). The same training samples that were employed in the traditional approach were used in the Gi approach.

A total of 200 randomly selected points with a minimum of 25 sample points per class were used to perform an accuracy assessment for the different window sizes of the

Gi approach. It was found that the 2004 image with the combination of all original bands and Gi-transformed bands 5, 4, 3 (using window size of 5x5) increased the overall accuracy to 94.5% from 91.5%. This was achieved through the use of the spectral bands alone. Therefore, the study used the combination of all original bands and Gi-

10 transformed bands 5, 4, 3 with a window size 5x5 for the land-use and land-cover classification of all images. Overall classification accuracies for the year 1985, 1991,

1995, 2000, and 2004 were found to be 90.0%, 93.5%, 94.0%, 90.5%, and 94.5% respectively. The classification accuracy of the Landsat images used in this study was above the minimum accuracy of 85% required by most resource management applications (Anderson et al., 1976).

Model Input Database

The SLEUTH model requires a binary map of urban and nonurban. The theme of urban extent was extracted from the classified land-use and land-cover map of the study area by assigning the pixel value of zero for nonurban and 255 for urban. The urban extent for the year 1985 was used as the seed to initialize the model and subsequent urban layers for the years 1991, 1995, 2000, and 2004 were used to calculate best-fit statistics for calibration.

Road layers were prepared for the 1985 and 2004 imagery. For 1985, a road map of the study area was digitized from a United States Geological Survey (USGS), 7.5’ series topographic sheet (Arizona-Sonora, SW/4 Nogales 15’ quadrangle). For the year

2004, a road map for the U.S. side was downloaded from the Census Bureau’s TIGER shapefiles and a road map for Nogales, Sonora was digitized from USGS, 7.5’ series

Topographic sheet (Arizona-Sonora, SW/4 Nogales 15’ quadrangle). The road network is a binary theme with all roads given a value of 100 and non-road pixels given a value of zero.

11 The exclusion layer defines the areas where urbanization can not occur, e.g. water bodies. The exclusion layer was extracted from the land-use and land-cover map for the year 2004. All water bodies were assigned a pixel value of 100 and all other pixel was assigned a value of zero.

The slope layer was derived from a USGS digital elevation model (DEM). The

DEM was acquired as a single scene from the USGS 30 meter DEM that covers both

Nogales, Arizona and Nogales, Sonora. However, a difference in resolution was found:

The resolution for U.S. side was much finer (30 m) than the Mexican side (90 m). The

DEM was transformed to percent slope and then truncated to integer values from floating point, which is required for the SLEUTH model input image format. The difference in

DEM resolutions for the U.S. side and the Mexican side could have potentially influenced the outcomes of both cities. It can be expected that the coarser resolution

DEM will likely lead to lower slope percents since the difference between elevation among neighborhood pixels are generalized. This situation might have created more favorable conditions for urban developments. This is because the slope coefficient influences all growth rules, as value increases, the likelihood of urbanized-steeper-slopes decreases.

The hill-shade layer was derived from the same USGS 30 meter DEM that was used to generate the slope layer. The hill-shade layer is used as a background image to give a topographic spatial context to the model image output.

Input Data Formatting

12 The SLEUTH model requires input data to be standardized in terms of format, dimension, projection, resolution, map extent, and naming format. All input layers were prepared in Erdas Imagine (raster format) and separated by country. U.S. images had the grid dimensions of 762 columns by 661 rows while the grid dimensions of the Mexico were 762 columns by 597 rows. All images had the resolution 28.50 meter, and were projected to UTM, WGS 84. The SLEUTH accepts input data in grayscale 8 bit Graphic

Image File (GIF) format, which is not an export option in Erdas Imagine software.

Hence, all the input layers were transformed first into TIF format and then converted into

GIF in Adobe Photoshop. A list of input data is given in Table 1 and the images are displayed in Figure 4 and 5.

Model Calibration

Calibration determines the best fit values for the five growth control parameters: dispersion coefficient, breed coefficient, spread coefficient, slope coefficient, and road gravity coefficient by fitting simulated data to historical spatial data. In the calibration process, the _start coefficient values initialize the first simulation then a coefficient value is increased by its _step value and another simulation is performed. The process is continued until the _stop value is reached or exceeded. This was repeated for all possible permutations for all given ranges and increments.

The five coefficients of the SLEUTH model range between zero and 100 and require extensive computation for calibration. A brute force method was used to calibrate the coefficient values. The methodology of brute force involves calibrating the model to the data in steps, sequentially narrowing the range of coefficient values and increasing the

13 data resolution. The calibration process was accomplished in three phases referred to as the coarse phase, the fine phase, and the final phase. During the calibration process, the

SLEUTH model generates best-fit statistics for eleven metrics, namely: compare, pop, edges, clusters, cluster_size, Lee-Sallee, slope, percent urban, x mean, y mean, and rad.

These metrics are generated for each control year. The simulated data is then compared to the metrics of the historical data and linear regression values are calculated. These best-fit values are written to the output file called control_stats.log, which is the main file used to score the many runs executed during each calibration phase.

A compare metric is run which examines the amount of modeled urban areas to known urban areas for the stop year. The pop, edges, clusters, cluster_size, slope, and percent urban are used to calculate the least squares regression for the modeled urban area, urban perimeter (edges), number of urban clusters, average cluster size, average slope of urbanized cells, and percent of available pixels urbanized compared to actual urban area variables. The Lee-Sallee metric measures the shape index, the spatial fit between the modeled urban growth and the known urban extent for the control year. The x mean and y mean metrics is used to calculate the least squares regression of average longitude and latitude respectively for modeled urbanized locations compared to the known urban locations for the control years. The last metrics, rad, measures urban dispersal.

The best coefficient sets can be found by sorting on one or more of the metrics contained in the control_stats log file. However, there is no definitive way of sorting these ranges. The various approaches may include sorting on all metrics equally, weighting some metrics more heavily than others, and sorting only one metric. The

14 algorithm for narrowing these ranges is a continuous topic of discussion among the users of Project Gigalopolis (2001). The coefficient sets in this research were selected by sorting by the Lee-Sallee metric.

Coarse Phase

The coarse phase of calibration explores the entire range (0 – 100) of the five coefficients using large increments. In this phase all the coefficients were set to (0-100,

25) where the first number (0) is the _start value, the second number (100) is the _stop value, and the third (25) is the _step value. The dataset’s full resolution was 28.50 meters, therefore, all the input images were resampled to 114 meters spatial resolution. A small value (4) was assigned for the number of Monte Carlo iterations. Given these conditions, the resultant number of iterations in this phase was 3,125. The best statistical fit measurements were stored in the control_stats log file. Using the control_stats log file, the top three ranking scores were identified by sorting the Lee-Sallee metric. The high and low values of the each coefficient were selected from the top three scores. The low values were set to _start and high values were set to _stop and the _step values were selected as an increment of 4-6 times between _start and _stop values. The selected coefficient ranges from the coarse calibration phase that were used to run the fine calibration phase are given in Table 4.

Fine Phase

The fine phase of calibration narrowed the coefficient ranges derived from the coarse phase were applied to the input data that were resampled to 57 meters spatial

15 resolution (half of its full size = 28.50 m). The goal for the fine phase is to further narrow down the coefficient ranges. The number of Monte Carlo iterations was then increased to

7 in order to reduce the level of errors. Given these conditions, the resultant number of iterations in this phase was 6,480. The best-fit coefficient values were selected from the control_stats log file using the top three scores by sorting only the Lee-Sallee metric. The selected coefficient ranges from the fine calibration phase that were used to run the final calibration phase are given in Table 4.

Final Phase

In the final phase of calibration the narrowed coefficient ranges, selected from fine phase were applied to the full resolution (28.50 m) input data. The goal for the final phase of calibration was to determine the best coefficient values. The number of Monte

Carlo iterations was increased to 10 in this phase. Given these conditions, the resultant number of iterations in this phase was 5,400. Using the control_stats log file, the coefficient values corresponding to the top score of the Lee-Sallee metric were identified.

In the case where more than one run had the same score, the lowest values for each coefficient was selected as the best coefficient values (Table 2 and 3). These values were set to _start and _stop values and the _step values were set to one. The selected coefficient ranges from the final calibration phase that were used to derive forecasting coefficients are given in Table 4.

Derive Forecasting Coefficients

16 For the SLEUTTH model the coefficient values that represent the starting values or the control coefficients, are drawn from the simulation end date values. Coefficient values generated in the final calibration phase can not be used to forecast future urban growth because of the SLEUTH model’s self-modification qualities which may alter coefficient values.

The best coefficient values derived from the final calibration phase were used to produce a single set of averaged coefficients for the stop date to initialize forecasting.

Since only one combination was applied for the computation, a large value (100) was assigned to the number of Monte Carlo iterations in order to minimize the level of errors.

The final values of the control coefficients were stored in the avg log file. These values, which were used to initialize a prediction run of the SLEUTH model, are given in Table

Model Simulation

Using the control coefficient values taken from the derived forecasting run, simulations were produced to predict urban growth from the past-to-present (1985 to

2004) as well as to simulate future urban growth from 2004 through 2025. The past-to- present simulations served as a visual verification for the accuracy of the model calibration as well as providing a historical account of urban development and landscape change. A large value (100) was assigned to the number of Monte Carlo iterations in order to minimize uncertainty in the simulation. The details of the control coefficients and the self-modification constraints used in this prediction run are given in Table 5. The prediction run generated both statistical and graphic outputs. The statistical measures

17 received from this prediction run are given in Table 6 under the column, ―scenario one.‖

The graphic outputs included an animated urban growth image which included an accumulated urban growth image for the stop year (2025) and yearly image predictive outputs for 2004 through 2025.

Planning Scenarios

Four planning scenarios were considered in this research by altering the composition of the SLEUTH model input layers to simulate the spatial consequences of urban growth: (1) business as usual; (2) environmental protection; (3) road network; and, an (4) anti-growth strategy. The purpose of these simulations was to investigate how various planning scenarios will affect urban growth patterns and landscape structures in the Ambos Nogales region. All these scenarios were simulated for the same time span i.e.

2004 to 2025. The number of Monte Carlo iterations was assigned with the value of 100 for each scenario. The business as usual, environmental protection and road network scenarios used the same control coefficient values for their prediction runs (Table 5).

Coefficient values were changed to simulate urban growth under the anti-growth scenario.

Business as Usual Scenario

In this business as usual scenario the same initial conditions that were used for the past to present (1985 to 2004) simulation were considered while other environmental and developmental conditions were not altered. Thus, this scenario provides a benchmark for comparison with other scenarios that consider alternative planning strategies.

Environmental Protection Scenario

This scenario protected environmentally sensitive lands, such as water bodies, national forest, national park, wetlands, and floodplains while maintaining other conditions used in the first scenario. For this scenario, two exclusion input layers were prepared for the U.S. and Mexican parts of the study area. For the U.S. portion, the exclusion layer was prepared using two shapefiles: (1) national parks and forest; and (2) federal and state government lands in Arizona. Both shapefiles derive from ESRI’s Data and Maps 2000 CD-ROM Set available as part of the ESRI software. Two major public lands were selected for exclusion: the Coronado National Forest; and the Patagonia Lake

State Park. In addition to these, all water bodies including, lakes, rivers, and streams, were excluded. Excluded areas were grouped together to form a binary excluded/non- excluded layer. For the Mexican portion of the study area, the exclusion layer relied on land-use maps (1997 to 2000) prepared by the Secretary of Urban Planning and Ecology of the Government of Sonora. Using these maps two classes were created: areas of ecological preservation; and, areas of ecological preservation where high restrictions were identified. In addition, all water bodies were excluded. All the excluded areas were assigned a pixel value of 100 and non-excluded areas had a pixel value of zero. The new excluded layers are displayed in Figure 6. While the environmental projection exclusion layer may seem standard for the U.S. portion, the Mexican portion of the study area can and does experience Colonia development on ecologically sensitive areas. Furthermore, on both side of the border, rivers and streams are intermittent and development has occurred in flood plains.

Road Network Scenario

The SLEUTH model simulates the tendency of urban development to locate where there is more accessibility due to transportation (Project Gigalopolis, 2001). The road network scenario examined if the relative weighting of roads would affect urban growth and land-use and land-cover change. The road network scenario used relative weighting of the roads according to their hierarchy and relative importance while maintaining other conditions used in the business as usual scenario. To prepare the road layer for the U.S. side, all highways (speed ≥ 60 mph) were assigned a pixel value of 100, and all major roads (speed ≥ 35 and < 60 mph) were assigned a value of 50, and all other streets (speed <35 mph) were given a value of 25. For Nogales, Sonora, roads that were categorized as undivided highways (Federal and State) were assigned a value of 100, while a second category called paved roads were given a value of 50 and the third category, unimproved roads, were given a value of 25. All non-roads were given a value of zero. The weighted road network layers used in this scenario are displayed in Figure 6.

Anti-Growth Scenario

This anti-growth scenario was based on slowing down the urban growth rate and altering the growth parameters while maintaining the road network and environmental protection scenarios. The purpose of this scenario was to examine if anti-growth strategies would have a different effect on urban growth and land-use and land cover change in the region.

20 Results and Discussion

The statistical measures of each scenario are given in Table 6. Tables 7 and 8 show the area and percent changes in land-use and land-cover classes and, provide information on how much land by area and percent of each class would be transformed into urban area from 2004 to 2025.

The business as usual simulation predicts that by 2025 urban land would increase by 315% in Nogales, Arizona and 145.0% in Nogales, Sonora. In both cities smaller urban cluster would grow outward and join other smaller cluster to make larger clusters, which represents massive organic (edge) growth. Exposed soil, followed by agriculture and grasslands would be the most highly susceptible to urban growth in all scenarios. The dramatic increase in urban growth can be observed from the model’s visual outputs for a single year. Some of these outputs for all scenarios are displayed in Figures 7, 8, and 9.

The business as usual scenario shows how massive urban growth would alter the landscape. The loss of forest, shrubs, and agriculture land in Nogales, Arizona is much greater than in Nogales, Sonora, however, massive growth on both sides of the border pose serious concerns in terms of ecological health, climate repercussion, and vulnerability of coupled human-environment system.

The environmental protection simulation predicts that by 2025 urban land would increase by 292.87% in Nogales, Arizona and 91.10% in Nogales, Sonora. This simulation also indicates the occurrence of massive organic (edge) growth in the cities.

The graphic outputs under this scenario are displayed in Figures 7, 8, and 9. Compared to the business as usual scenario, the environmental protection scenario preserved 394 ha

(3.1%) of forest area, 405 ha (2.12%) of shrubs, and 4 ha (0.8%) of agriculture area in

21 Nogales, Arizona and 319 ha (3.6%) of forest area, 1297 ha (5.4%) of shrub land, and 79 ha (16%) of agriculture area in Nogales, Sonora. This scenario shows the importance of environmental protection for future urban development and planning in the Ambos

Nogales region.

The road network simulation predicts that by 2025 urban land would increase by

314.58% in Nogales, Arizona and 145.59% in Nogales, Sonora. As with the first two simulations, this one predicts enormous organic (edge) growth throughout the region. The road network scenario results are very similar to the business as usual scenario, especially in Nogales, Arizona where there was a slight decrease in organic growth, and more preservation of forest, shrubs, and agricultural land.

The anti-growth scenario examined the effects of slowing down the growth rate and altering growth parameters while maintaining the environmental protection and weighted road network conditions used in those two scenarios. In the previous scenarios

(Table 6), more than 99.0% of urban growth was organic. The anti-growth scenario restrained organic growth. Organic growth was cut to 90.3% and 93.3% in Nogales,

Arizona and Nogales, Sonora respectively. In addition, more residential growth should be encouraged in future simulations because low-density urban use (mainly residential) tends to develop away from existing large urban facilities in the region. In order to examine this concept, we altered some growth parameters under the anti-growth scenario.

The starting value of the spread coefficient was reduced to 6 from 24 but the diffusive coefficient was increased to 25 from 1. In addition, the breed coefficient was increased to

25 from 2 in Nogales, Arizona and to 50 from 25 in Nogales, Sonora. Since the road- influenced growth accounts for a small share of total growth in the business as usual,

22 environmental protection, and road network scenarios, the road gravity coefficient was increased to 50 from 36 in Nogales, Arizona and from 23 in Nogales, Sonora.

The anti-growth simulation predicts that by 2025 urban land would increase by

218.85 % in Nogales, Arizona and 29.75% in Nogales, Sonora. The results under each scenario demonstrate that Nogales, Arizona would have much more urban growth than that of Nogales, Sonora by 2025 (Table 9 and 10). We believe this is an important growth pattern, and urban planners and policy makers need to pay attention to this issue. We do not fully understand why the rate of change from all other land categories to urban in

Nogales, Arizona is higher than that of Nogales, Sonora. This is probably due to the fact that the pattern of infrastructure especially road networks in Nogales, Arizona is more widely and evenly distributed than those in Nogales, Sonora. The elevation and slope in relation to different land-cover classes could have played an important role in this change as well. It is important to note that the ratios between forest converted to urban in

Nogales, Arizona and Nogales, Sonora for scenario 1, 2, 3, and 4 by 2025 would be 7.2,

15.3, 7.1, and 46.3 respectively. This implies that the conversion from forest to urban in

Nogales, Arizona would be significantly higher than Nogales, Sonora. We consider this a serious issue in the future urban planning of Nogales, Arizona. Ratios between shrubs land cover converted to urban in Nogales, Arizona and Nogales, Sonora for scenario 1, 2,

3, and 4 would be 2.1, 3.2, 2.1, and 7.9 respectively. It should be noted that conversion from shrubs to urban areas in Nogales, Arizona would be 2 to 8 times higher than

Nogales, Sonora depending on the scenario considered (2.1, 3.1, 2.1, and 7.9 for scenario

1, 2, 3, and 4 respectively). In contrast to this, much less open land area (exposed soil category) in Nogales, Arizona (426 ha, 421 ha, 425 ha, and 251 ha for scenario 1, 2, 3,

23 and 4 respectively) would be converted to urban by 2025 whereas significantly larger exposed soil areas (2,143 ha, 1,987 ha, 2,144 ha, and 1,435 ha for scenario 1, 2, 3, and 4 respectively) were converted to urban in Nogales, Sonora. In this case, the conversion in

Nogales, Arizona is more desirable as open land spaces would be converted to urban.

This implies that there would be a considerable amount of environmental degradation in

Nogales, Sonora by 2025 in comparison to Nogales, Arizona that can significantly affect the urban heat island, urban storm water pollution, carbon release, water consumption, health risks, air pollution, ground water pollution, and land degradation that can consequently lead to desertification. However, it should be noted that urbanization, in terms of area extent, in Nogales, Arizona is higher than Nogales, Sonora.

In order to investigate the best planning scenario the study used the business as usual model as a benchmark for comparison with the other three scenarios. The road network scenario slightly decreased organic growth in Nogales, Arizona and preserved forest, shrubs, and agriculture land. But, the road network scenario did not affect

Nogales, Sonora. Although the environmental protection scenario preserves a small percent of green spaces, the overall results from the first three scenarios (business as usual, environmental protection, road network) demonstrated that unchecked urban growth along with numerous edge developments would substantially alter the forest, shrubs, agriculture and grassland in the Ambos, Nogales region. The anti-growth scenario preserved more land than the environmental protection scenario on both sides of the border and therefore it is considered the most desirable option for planning future urban growth in Ambos Nogales. It should be noted that the total land area converted to urban in Ambos Nogales (both cities together) for scenario 1, 2, 3, and 4 would be 18,114 ha,

24 15,456 ha, 18,082 ha, and 10,416 ha respectively. This confirms that the anti-growth scenario is considered the most advantageous. However, conversion from forests to urban in Nogales, Sonora would be only 2% of the forests converted to urban in Nogales,

Arizona. In contrast to this, conversion from open land to urban in Nogales, Sonora would be 6 times smaller than that of Nogales, Arizona. This type of conversion in

Nogales, Arizona is more desirable. Under all scenarios, the conversion of agricultural areas to urban areas would be greater in Nogales, Arizona (298 ha, 298 ha, 294 ha, 296 ha, and 203 ha for scenario 1, 2, 3 and 4 respectively) than in Nogales, Sonora (107 ha,

28 ha, 107 ha, and 8 ha for scenario 1, 2, 3, 4 respectively). This suggests that there is a need to formulate better policy and planning strategies and law enforcement actions to protect forests in Nogales, Arizona and encourage more developments and urbanization in open land areas in Nogales, Sonora. One other option would be to encourage conversion from agriculture to urban by introducing intensive agriculture practices to increase the production or at least keep the same level of production with smaller agricultural areas.

The study revealed that unchecked urban growth trends in the first three scenarios simulated enormous (99.5%) edge developments or organic growth throughout the region. The organic growth would alter the forests, shrubs, agriculture, and grasslands substantially despite the implementation of environmental protection and weighted road network strategies in the second and third scenario respectively. In contrast, the anti- growth strategy in the fourth scenario encouraged spontaneous, diffusive, and road influenced growth and preserved more green and open spaces, including national forest, national park, water bodies, agriculture, grassland and shrubs. In general, other land

25 categories converted to urban in Ambos Nogales using the fourth scenario would be significantly lower than the first three scenarios. For example, agriculture, forest, shrubs, exposed soil converted to urban using the first scenario would be 404 ha, 4,490 ha,

10,649 ha, and 2,570 ha respectively whereas the same land conversion using the fourth scenario would be 210 ha, 2,663 ha, 5,855 ha, and 1,686 ha respectively. In general, the anti-growth scenario could preserve about 50% of the forests and shrubs in the region.

Therefore, the last scenario that emphasizes smart growth and environmental protection is the most desirable for future urban development and planning in Ambos Nogales.

Arizona’s border communities are interconnected economically, politically, and socially with their sister cities in Sonora owing to their bi-national heritage. Thousands of people and vehicles cross the border daily to work, shop, attend school, and visit family.

The air they breathe, the water they use, and the waste they generate are shared.

Therefore the overall problems that affect infrastructure development and quality-of-life issues are vital. Although the simulated spatial patterns of urban growth for 2025 were very different for the paired cities, with more urban growth on the Arizona-side, the region as a whole will have lots of urban growth coupled with the loss of green spaces.

Results from this study suggest the need for both cities to work together to formulate planning strategies and policies for future smart growths that would achieve sustainable development in the region. We believe that the two cities must work together to formulate better management plans to achieve healthy environment and better economic development since socio-ecological systems and ecosystem services in both cities and their surrounding environments are strongly interconnected and highly interdependent.

The findings of this study can be expected to be useful to the local and regional

26 governments on both sides of the border to assess risks of environmental degradation, ecological health, climate repercussion, and vulnerability of coupled human-environment systems and, aide in the development of bi-national management strategies.

Conclusion

This study has demonstrated the effectiveness of the SLEUTH model for urban land use and planning. The calibration results in the context of Ambos Nogales have proved the model’s portability and universality of application. The SLEUTH model’s strength relies on the fact that it can incorporate urban extent, transportation gravity, slope resistance, along with four types of urban growth (spontaneous, new spreading centers, organic, and road influenced). In addition, the model proves that it is capable of incorporating different locational conditions, such as road networks with various weights and different environmental protection definitions. These properties present the significant potential for modeling urban growth and land-use and land-cover changes under different planning scenarios by altering some initial conditions and changing input data.

The study has examined the spatial consequences of urban growth on landscape change under four different scenarios in the paired border cities of Nogales, Arizona and

Nogales, Sonora. Each scenario demonstrates that Nogales, Arizona would have much larger urban growth and loss of green spaces than that of Nogales, Sonora by 2025. The first scenario (business as usual) simulates the massive urban growth and huge loss of forest, shrubs, and agriculture land in Ambos Nogales if the current rate and pattern of urban growth is not altered. The second scenario (environmental protection) is important

27 because it preserves a small percent of green spaces. The results from the third scenario

(road network) are quite similar to the business as usual scenario except it slightly decreased organic growth and preserved some green spaces. The study reveals that the unchecked urban growth trend in the business as usual, environmental protection, and road network scenarios simulate enormous (99.5%) edge developments or organic growth throughout the region. In contrast, the anti-growth scenario allows for more green and open space and is therefore the most desirable for planning future urban land use and development.

The technical frameworks developed in this research can be deployed to simulate future urban growth for other international border cities. These findings can contribute to bi-national planning communities, municipalities, countries, and other public and private organizations that need to manage resources and provide services to people living in rapidly changing paired border cities. Furthermore, this research can be extended to other paired U.S.-Mexico border cities for achieving desired smart and responsible urban growth and sustainable development.

Acknowledgement:

The study was supported by the Southwest Consortium for Environmental Research and

Policy (CERP FY2006) Applied Border Environmental Research Program (Grant # EIR-

05-04). The authors would like to thank Subhro Guhathakurta and Jana Hutchins for their valuable suggestions and supports. We are also grateful for the comments and suggestions of anonymous reviewers that significantly improved the manuscript.

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Table 1. List of model input data

Theme Year Source SLEUTH Naming Format Schedule Files Urban Extent 1985 Landsat (TM) image location.urban.date.gif AZnog.urban.1985.gif SNnog.urban.1985.gif 1991 Landsat (TM) image location.urban.date.gif AZnog.urban.1991.gif SNnog.urban.1991.gif 1995 Landsat (TM) image location.urban.date.gif AZnog.urban.1995.gif SNnog.urban.1995.gif 2000 Landsat (TM) image location.urban.date.gif AZnog.urban.2000.gif SNnog.urban.2000.gif 2004 Landsat (TM) image location.urban.date.gif AZnog.urban.2004.gif SNnog.urban.2004.gif Transportation 1985 USGS Topographic sheet (SW/4 location.roads.date.gif AZnog.roads.1985.gif Nogales 15’ quadrangle) SNnog.roads.1985.gif 2004 United States Census Bureau’s location.roads.date.gif AZnog.roads.2004.gif TIGER , Exclusion 2004 Landsat (TM) images location.excluded.gif AZnog.excluded.gif SNnog.excluded.gif Slope USGS 30 m DEM location.slope.gif AZnog.slope.gif SNnog.slope.gif Hillshade USGS 30 m DEM location.hillshade.gif AZnog.hillshade.gif SNnog.hillshade.gif

Table 2. Top most score from control_stats_log file (generated in final calibration phase) for Nogales, Arizona, sorting only on the Lee Sallee metric

Cluster Run Compare Pop Edges Clusters Leesalee Slope %Urban Xmean Ymean Rad Diff Brd Sprd Slp RG Size 217 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 30 34 223 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 32 34 229 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 34 34 235 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 36 34 241 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 38 34 247 0.90 0.76 0.71 0.94 0.14 0.30 0 0.95 0.98 0.70 0.74 1 2 20 40 34

Table 3. Top most score from control_stats_log file (generated in final calibration phase)

for Nogales, Sonora, sorting only on the Lee Sallee metric

Cluster Run Compare Pop Edges Clusters Leesalee Slope %Urban Xmean Ymean Rad Diff Brd Sprd Slp RG Size 868 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 15 21 874 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 17 21 880 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 19 21 886 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 21 21 892 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 23 21 898 0.88 0.73 0.45 0.16 0.34 0.40 0 0.45 0.28 0.24 0.77 1 21 20 25 21

Table 4. The resolution of the data, number of Monte Carlo iterations, and the coefficient values used in the Calibration runs and Derive forecasting run

Calibration Runs Derive Coarse Fine Final Forecasting Run Nogales, Noagles, Nogales, Noagles, Nogales, Noagles, Nogales, Noagles, AZ SN AZ SN AZ SN AZ SN Resolution (m) 114 57 28.5 28.5 Monte Carlo iterations 4 7 10 100 Diffusion_start_coeff 0 0 0 0 1 1 1 1 Diffusion_step_coeff 25 25 5 5 1 1 1 1 Diffusion_stop_coeff 100 100 20 20 5 5 1 1 Breed_start_coeff 0 0 0 0 1 1 2 21 Breed_step_coeff 25 25 5 5 1 5 1 1 Breed_stop_coeff 100 100 25 25 5 25 2 21 Spread_start_coeff 0 0 25 25 20 20 20 20 Spread_step_coeff 25 25 5 5 1 1 1 1 Spread_stop_coeff 100 100 50 50 25 25 20 20 Slope_start_coeff 0 0 25 0 30 15 30 15 Slope_step_coeff 25 25 5 5 2 2 1 1 Slope_stop_coeff 100 100 50 25 40 25 30 15 Road_start_coeff 0 0 0 0 30 1 34 21

Road_step_coeff 25 25 10 13 4 5 1 1 Road_stop_coeff 100 100 50 75 50 26 34 21

Table 5. The resolution of the data, number of Monte Carlo iterations, and the coefficient values used in the Prediction run

Nogales, Nogales, Prediction Runs AZ SN Resolution (m) 28.5 Monte Carlo iterations 100 Self-modification critical_high 1.3 constraints critical_low 0.97 boom 1.01 bust 0.09 critical_slope 15

Control coefficients diffusion 1 1 breed 2 25 spread 24 24 slope resistance 6 1 road gravity 36 23

Table 6. Statistical measures received under different scenarios

Past to present Future Simulations (2025) simulation (2004) Statistical Scenario 1 Scenario 2 Scenario 3 Scenario 4 measures Nogales, Nogales, Nogales, Nogales, Nogales, Nogales, Nogales, Nogales, Nogales, Nogales, AZ SN AZ SN AZ SN AZ SN AZ SN sng 6.19 5.31 5.01 5.09 3.87 1.8 5.28 4.94 40.1 0.24 sdg 0.42 2.5 0.2 3.12 0.12 0.96 0.14 2.49 23.81 0 og 2057.09 1547.81 4830.65 1538.65 4505.12 952.89 4822.74 1542.77 1144.18 32.68 rt 7.07 18.48 4.63 5.7 4.03 7.22 5.01 5.5 59.29 2.11 pop 55104.47 46179.24 135855 65571.3 131057 57277.7 135909 65561.7 74575.4 39155.5 edges 22483.01 17115.56 42775.9 13640.8 41186.5 10624.5 42771.8 13648.7 36517.4 9863.36 clusters 2162.28 1957.17 2995.38 1046.11 2925.04 824.75 3002.61 1048.56 4686.73 815.16 cl_size 24.88 23 44.73 62.01 44.17 68.79 44.63 61.84 15.2 47.39 diffusion 1.2 1.2 1.22 1.22 1.22 1.22 1.22 1.22 12.2 0.09 spread 23.92 23.92 29.28 29.28 29.28 29.28 29.28 29.28 7.32 0.09 breed 2.39 25.12 2.44 30.5 2.44 30.5 2.44 30.5 30.5 1 slope resistance 6.30 1 1 1 1 1 1 1 1 77.12 road gravity 36.37 22.87 41.66 26.35 44.1 31.69 42.6 26.35 55.62 42.39 %urban 20.63 15.69 38.11 20.16 47.3 49.65 37.8 20.17 31.31 38.29 grw_rate 3.76 3.41 3.56 2.37 3.44 1.68 3.56 2.37 1.7 0.09 grw_pix 2070.77 1574.1 4840.49 1552.56 4513.14 962.87 4833.17 1555.7 1267.38 35.03

Note: sng = the number of new urban pixels generated from spontaneous growth; sdg = the number of new urban pixels generated from new spreading center growth; og = the number of new urban pixels generated from edge growth; rt = the number of new urban pixels generated from road influenced growth; pop = the total number of urban pixels; edges = the total number of urban/non-urban pixel edges; clusters = the total number of urban clusters; cl_size = average urban cluster size; %urban = Percent of the number of urban pixels divided by the total number of pixels in the study area (nrows*ncols) minus the number of pixels that are completely excluded from urban growth; grw_rate = Percent of the new urban pixels in one year divided by the total number of urban pixels: (100 * num_growth_pix / pop); grw_pix = total number of new urban pixels.

Table 7. Cross-section areas (hectares) of land-use and land-cover for the year 2004, projected areas for the year 2025, and percent change from 2004 to 2025, under different scenarios: Nogales, Arizona

Scenario 1 Scenario 2 Scenario 3 Scenario 4

2004 2025 2025 2025 2025

LULC Area Area Change Area Change Area Change Area Chang classes (ha) (ha) (%) (ha) (%) (ha) (%) (ha) e (%)

U 4313 17902 315.07 16945 292.87 17881 314.58 13754 218.89 A/G 482 184 -61.83 187 -61.10 186 -61.47 279 -42.07 F 12750 8811 -30.89 9204 -27.81 8812 -30.88 10143 -20.45 S 19160 11951 -37.63 12355 -35.51 11970 -37.53 13964 -27.12 ES 4085 1942 -52.47 2097 -48.65 1941 -52.49 2650 -35.13 W 122 122 0.00 122 0.00 122 0.00 122 0.00

Note: LULC = Land-use and land-cover; U = Urban; A/G = Agriculture/Grassland; F =

Forest; S = Shrubs; ES = Exposed Soil; W = Water.

Table 8. Cross-section areas (hectares) of land-use and land-cover for the year 2004, projected areas for the year 2025, and percent change from 2004 to 2025, under different scenarios: Nogales, Sonora

Scenario 1 Scenario 2 Scenario 3 Scenario 4

2004 2025 2025 2025 2025

LULC Area Area Change Area Change Area Change Area Chang classes (ha) (ha) (%) (ha) (%) (ha) (%) (ha) e (%) U 3100 7625 145.95 5924 91.10 7614 145.59 4075 29.75 A/G 489 382 -21.83 461 -5.67 382 -21.93 480 -1.65 F 8818 8267 -6.25 8586 -2.63 8262 -6.31 8762 -0.63 S 24073 20633 -14.29 21930 -8.90 20649 -14.23 23414 -2.57 ES 461 35 -92.49 40 -91.30 36 -92.28 210 -52.06 W 9 9 0.00 9 0.00 9 0.00 9 0.00

Note: LULC = Land-use and land-cover; U = Urban; A/G = Agriculture/Grassland; F =

Forest; S = Shrubs; ES = Exposed Soil; W = Water.

Table 9. Land transformation of other land-use and land-cover classes into urban area from 2004 to 2025, under different scenarios: Nogales, Arizona

Scenario 1 Scenario 2 Scenario 3 Scenario 4

From/To 2025 U 2025 U 2025 U 2025 U

Area (Ha) % Area (Ha) % Area (Ha) % Area (Ha) % 2004 A/G 298 61.83 294 61.10 296 61.47 203 42.07 2004 F 3939 30.89 3546 27.81 3938 30.88 2607 20.45 2004 S 7209 37.63 6804 35.51 7190 37.53 5196 27.12 2004 ES 2143 52.47 1987 48.65 2144 52.49 1435 35.13

Note: U = Urban; A/G = Agriculture/Grassland; F = Forest; S = Shrubs; ES = Exposed

Soil.

Table 10. Land transformation of other land-use and land-cover classes into urban area from 2004 to 2025, under different scenarios: Nogales, Sonora

Scenario 1 Scenario 2 Scenario 3 Scenario 4

From/To 2025 U 2025 U 2025 U 2025 U

Area (Ha) % Area (Ha) % Area (Ha) % Area (Ha) % 2004 A/G 107 21.83 28 5.67 107 21.93 8 1.71 2004 F 551 6.25 232 2.63 556 6.31 56 0.64 2004 S 3440 14.29 2143 8.90 3425 14.23 659 2.74 2004 ES 426 92.49 421 91.30 425 92.28 251 54.51

Note: U = Urban; A/G = Agriculture/Grassland; F = Forest; S = Shrubs; ES = Exposed

Soil

Spontaneous Growth New Spreading Centers Growth f (dispersion coefficient, slope f (breed coefficient, slope coefficient) coefficient)

Edge Growth Road Influenced Growth f (spread coefficient, slope f (spread, breed, slope, dispersion, and coefficient) road gravity coefficient)

Figure 1. Growth rules (Modified from Project Gigalopolis (2001)).

Figure 2. Study Area: Nogales, Arizona and Nogales, Sonora.

(a) Landsat Band 4, 3, 2 - 1985 (b) Landsat Band 4, 3, 2 - 1991 (c) Landsat Band 4, 3, 2 - 1995

(d) Landsat Band 4, 3, 2 - 2000 (e) Landsat Band 4, 3, 2 - 2004

Figure 3. Landsat TM band 4 (near infrared band), band 3 (red band), and band 2(green band) displayed in red, green and blue of the study area: (a) 20 October 1985; (b) 1 July

1991; (c) 2 February 1995; (d) 3 September 2000; (e) 20 July 2004.

Urban - 1985 Urban - 1991 Urban - 1995

Urban - 2000 Urban - 2004 Excluded Areas

Roads - 1985 Roads - 2004

Slope Hillshade

Figure 4. Input image datasets for Nogales, Arizona.

Urban - 1985 Urban - 1991 Urban - 1995

Urban - 2000 Urban - 2004 Excluded Areas

Roads - 1985 Roads - 2004

Slope Hillshade

Figure 5. Input image datasets for Nogales, Sonora.

Scenario 2 Scenario 3

Excluded - Nogales, Arizona Nogales, Arizona Nogales, Arizona Road weight - 1985 Road weight - 2004

Excluded - Nogales, Sonora Nogales, Sonora Nogales, Sonora Road weight - 1985 Road weight - 2004

Figure 6. New excluded layers used under scenario 2 and weighted road network layers used under scenario 3.

53 2005 2015 2025

Scenario1 Scenario1

ScenarioScenario22

ScenarioScenario33

ScenarioScenario4 4

Figure 7. Simulation of future urban growth under four different scenarios in Nogales,

Arizona.

2005 2015 2025

Scenario 1 Scenario 1

Scenario 2 Scenario 2

ScenarioScenario 33

ScenarioScenario 4 4

Figure 8. Simulation of future urban growth under four different scenarios in Nogales,

Sonora.

Nogales, Arizona - 2025 Nogales, Sonora - 2025

Scenario 1 Scenario

Scenario 2 Scenario

Scenario 3 Scenario Scenario 4 Scenario

Figure 9. Simulation of the spatial consequences of urban growth and landscape change by 2025 in Nogales, Arizona and Nogales, Sonora. Note: Urban = white; Agriculture and grassland = Light green; Forest = dark green; Shrubs = yellow; Exposed Soil = brown;

Water = blue.