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The accuracy of IUMAT to analyze and evaluate the impact of urban metabolism depends on the availability of microscale data. One primary challenge for this project was to develop methodologies to generate data for missing microscale databases. Gathering and organizing an appropriate microscale data for IUMAT was beyond the scope of this study. But, simple techniques sometimes were implemented for generating the missing and necessary data. For example, IUMAT-LUM simulates the land-use transition from 1971- 2005 in the town of Amherst, but LIDAR data was only available for 2005. Since the probability of rebuilding a structure is negligible in Amherst, we assumed that buildings are the same from 1971 to 2005. Therefore, by using land-use data, building information measurements was used to regenerate LIDAR data for 1985 and 1971. For more complex parameters, we use national and regional databases to formulate the general framework, relations, and analytical models. Some coefficients would be altered if IUMAT models were implemented in other locations. By using UMass Amherst and Amherst City as a case study for this research, we demonstrated that the IUMAT holistic framework can be implemented for urban metabolism analysis, but the same framework is not applicable for using in another urbanscape.

Artificial Neural Networks (ANNs) are embedded in the modeling structure of the IUMAT-LUM. ANNs are interconnected networks of simple units similar to human neural systems that apply the mathematical logic capacity to solve sophisticated problems such as land-use changes and urban growth. ANNs do not make any assumption about data distribution and decrease the degree of subjectivity in modeling complex phenomena. One of the main weaknesses of ANNs models is the “black box” behavior. In many cases, users cannot extract specific rules from the modeling process. For simulating complex phenomena

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such as land-use change, which requires wide ranges of variables, the model recognizes patterns in data rather than finding unique relations. Researchers cannot derive definite causality between land-use pattern change and an independent variable from IUMAT-LUM results. Also, the effects of independent variables might alter from one land-use type to another class (Basse et al., 2014); therefore, single ANNs model cannot solely be adequate for simulating different land-use types (Carrero et al., 2014; Tayyebi & Pijanowski, 2014). Due to the complexity of ANNs modeling structure and time limitation for this dissertation, IUMAT-LUM employs one ANNs model for predicting the land-use change. This modeling structure limits the capacity of the IUMAT-LUM in formulating and analyzing different planning scenarios. ANNs models have a tendency for overfitting the data. This characteristic could be regarded both as a potential and a weakness. In the current state of IUMAT-LUM, the model cannot be overfitted to one particular land-use type, since it recognizes the overall patterns of change.

The algorithms of IUMAT-LUM are written in Python to generate, process, and analyze data. Python is an object-oriented programming language with a dynamic interpretation. This programming language supports packages and modules, and is very attractive for rapid application development. Since there is no compilation step in the computing process, the edit-test-debug cycle is relatively fast and effective when compared to other programming languages. These characteristics make Python a suitable candidate for developing IUMAT-LUM framework. But Python is a high-level language and allows the programmer to develop algorithms closer to how humans think. This particular character makes Python codes 10 to 100 times slower than other low-level languages such as C++. The IUMAT-LUM requires significant computing power that was not available at the time of this

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dissertation; therefore, numbers of validation and testing of the proposed model were limited in this study.

In many cases, changes of involving parameters in land-use modeling are not limited to the boundaries of urbanscape. For example, demographic changes in city-center neighborhoods might bring more housing developments in suburban districts. Some of the existing models appropriately consider this issue in their methodologies. But it is challenging to analyze land-use transition if the microscale data is not available outside city boundaries. Recognizing this obstacle, IUMAT-LUM simulates land-use change only within borders of the town of Amherst.

IUMAT-LUM, at the current stage, should be regarded as a pilot project until other external validation measures are conducted. The town of Amherst was selected as the only case study for testing the IUMAT-LUM framework and the idea of how urban form indices affects land-use transitions. Although the results provide enough evidence for such a causal relationship between building form and land-use change, I cannot draw any general conclusion. The results might be helpful in forming a new hypothesis that can be tested in different locations. Big cities such as Austin, Denver, and Seattle with a higher rate of urban growth and more comprehensive databases compared to Amherst are appropriate future case studies. In doing so, we can also integrate other demographic, employment, and economic parameters in future analysis. Other independent variables such as different public transportation systems, tree canopy, and flood zone restriction clearly influence the outcomes of IUMAT-LUM, but this model did not specifically test them. Besides, impacts of building geometries indices on the land-use modeling can be explored through a variety of building and urban morphologies in metropolitan areas. Since the major part of this study was to develop the modeling framework of IUMAT-LUM, outcomes of the proposed model did not

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compare with other existing models for validation. In addition, no public or elected officials have reviewed the IUMAT-LUM framework, so the practicality and usefulness for professional applications were not yet verified.

At this stage of the research, we have focused on urban metabolism in separate models without connecting them. One of the main goals behind this endeavor was to create a framework to analyze an urban area as a single entity and simulate urban metabolism by taking major urban subsystems into the modeling without directly dividing them. Currently, the IUMAT Land-Use and EMW models have been in development. Both models share similar approaches and principles that will be combined in the future of this collaborative research.