**Meet the editors**

Jorge Rocha is a geographer and was born in Lisbon in 1970. He has an MSc in Geographic Information Systems (2003) and in Spatial Planning (2013) and a PhD in Geographic Information Science (2012). He is currently an Assistant Professor at the Institute of Geography and Spatial Planning and a member of the Modelling, Urban and Regional Planning, and Environmental Hazard and

Risk Assessment and Management research groups at the Centre of Geographical Studies, all at the University of Lisbon. His fields of expertise are Geosimulation and Geocomputation involving Artificial Neural Networks, Graphs Theory, Cellular Automata, and Multi-agent Systems. Jorge's works are quite diverse, focusing mainly on, but not exclusively, in Urban Morphology, Remote Sensing, Epidemiology, Health Geography, Smart Cities and Big Data (Geomarketing and Tourism).

José António Tenedório is a geographer and an associate professor at the Universidade NOVA de Lisboa, NOVA FCSH, Lisbon, Portugal. He is also a Visiting Professor at Polytechnic University of Catalonia, Barcelona Tech, Barcelona School of Architecture (ETSAB), Spain. He graduated with a degree in Geography and Regional Planning. He attended the Sorbonne University, Faculty

of Sciences (UPMC), where he undertook postgraduate studies (DESS) in Remote Sensing. At the University of Paris-Est Créteil (UPEC), he obtained his PhD in Urbanism. His main scientific areas of research are Urban Remote Sensing, Spatial Analysis, and Geographical Modeling. He has published more than one hundred papers (book chapters, papers in periodicals with scientific refereeing, and papers in conference proceedings with scientific refereeing). He was won the first prize in the SANTANDER Award for the Internationalization of the NOVA FCSH Scientific Production (2013 and 2015 (*ex aequo*)).

Contents

**Preface VII**

**Planning 3**

Chapter 1 **Introductory Chapter: Spatial Analysis, Modelling, and**

Chapter 2 **One World, One Health Challenge: The Holistic Understanding**

**of Rickettsiosis Integrating Multi-Criteria Analysis Techniques**

Diego Montenegro, Ana Paula da Cunha, Ingrid Machado, Liliane Duraes, Stefan Vilges de Oliveira, Marcel Pedroso, Gilberto S.

José António Tenedório and Jorge Rocha

**Section 2 Data Analytics and Spatial Analysis 17**

**and Spatial Statistics 19**

**2001 to 2017 39**

**Maryland 79**

Gazêta and Reginaldo P. Brazil

Chapter 3 **Spatial Analysis of Bifenthrin Sediment and Water**

Lenwood W. Hall and Ronald D. Anderson

**High Resolution Satellite Images 61** Ana Cristina Gonçalves and Adélia M. O. Sousa

Chapter 5 **Evaluating the Effectiveness of CCTV in Baltimore,**

Brian Ways and Brooks C. Pearson

**Concentrations in California Waterbodies from**

Chapter 4 **Absolute Density Measures Estimation Functions with Very**

**Section 1 Introduction 1**

## Contents

### **Preface XI**



Diego Montenegro, Ana Paula da Cunha, Ingrid Machado, Liliane Duraes, Stefan Vilges de Oliveira, Marcel Pedroso, Gilberto S. Gazêta and Reginaldo P. Brazil


### Chapter 6 **The Use of Photos of the Social Networks in Shaping a New Tourist Destination: Analysis of Clusters in a GIS Environment 95** Hélder Tiago da Silva Lopes, Paula Cristina Almeida Cadima Remoaldo and Vitor Ribeiro

Preface

The academic community, companies, and local and central administration all use digital data extensively. New and powerful technology solutions, including geographic informa‐ tion systems (GIS), have been evolving to enhance spatial data analysis. Spatial analysis is a type of analysis based on geographic exploratory techniques (data mining) and confirmato‐ ry facts and phenomena with space expression, contributing to policy issues and spatial planning. Currently, spatial analysis has acquired more importance than ever, due to the large volumes of spatial data (big data) available from different sources, such as social net‐ works and mobile phones. Moreover, one now has the opportunity to establish relationships

Strictly speaking, spatial analysis can be seen as a way of analyzing spatial data, that is, turning data into information. In broad terms, it consists of enlightening and describing the processes and structures of spatial phenomena. It is the heart of GIS because it comprises the changes, uses, and procedures that can be applied to geographic data, adding value, expos‐ ing patterns and outliers that are not instantaneously recognizable, and creating new infor‐ mation that provides new insights and supports decisions. It embraces a diversity of data processing functions designed for deriving spatial relationships, patterns, and trends that are implicit in the data. The results could be used immediately on spatial problem solving and decision-making or as input for posterior spatial analysis and modeling. Eventually, spatial analysis is used for decision-making, to assist spatial planning and to support the

Spatial modeling is an analytical procedure usually used in connection with a GIS in order to define the processes and properties of spatial features: it is a crucial technique of spatial analysis. Using models, within a GIS environment, for analyzing spatial data, one can accu‐ rately analyze the output data for an enhanced understanding by nonspecialists. Its spatial nature aids researchers in understanding the data and explaining the conclusions that are tough to express based on numeric and/or text data alone. The final goal of spatial modeling is to be able to simulate geographic phenomena in order to facilitate problem solving and/or planning. The information handling is done in numerous steps, each one of them represent‐

This book aims to explain both Geographic Information Science and Spatial Science using a group of twelve unique research contributions that reflect some of the state-of-the-art ad‐ vances in spatial analysis and location modeling. The incorporated research goes from theo‐ retical to applied work, passing through some methodological insights. It is subdivided into three main parts: the first focuses on the research of Knowledge Discovery, Geographic Data Mining, Multi-Scale Representation and Spatial Analysis, the second studies aspects of, Spa‐

and mathematical statistics in time and space (geosimulation).

creation of management policies.

ing a phase in a complex analysis process.


Chapter 8 **Quantification and Prediction of Land Consumption and Its Climate Effects in the Rhineland Metropolitan Area Based on Multispectral Satellite Data and Land-Use Modelling 1975–2030 151** Andreas Rienow, Nora Jennifer Schneevoigt and Frank Thonfeld


## Preface

Chapter 6 **The Use of Photos of the Social Networks in Shaping a New Tourist Destination: Analysis of Clusters in a GIS**

Hélder Tiago da Silva Lopes, Paula Cristina Almeida Cadima

**Environment 95**

**VI** Contents

**Sets and GIS 115** Khalid Al-Ahmadi

**1975–2030 151**

**Engineering 169** Toshiaki Ichinose

Andreas Koch

Remoaldo and Vitor Ribeiro

**Section 3 Spatial and Spatiotemporal Modelling 113**

Chapter 9 **Fusion Study of Geography and Environmental**

Chapter 7 **Modelling Driving Forces of Urban Growth with Fuzzy**

Chapter 8 **Quantification and Prediction of Land Consumption and Its**

Chapter 10 **Generating Reality with Geosimulation Models: An Agent-**

Chapter 11 **Formal Urban Dynamics, Policy and Implications on Urban Planning: Perspectives on Kampala, Uganda 211**

Chapter 13 **Political Economy and the Work of Kenneth Arrow 243**

**Section 4 Geographic Knowledge and Spatial Planning 209**

John J. Williams and Fred Bidandi

Chapter 12 **Risk Analysis and Land Use Planning 229**

Valentina Svalova

Norman Schofield

**Multispectral Satellite Data and Land-Use Modelling**

**Climate Effects in the Rhineland Metropolitan Area Based on**

Andreas Rienow, Nora Jennifer Schneevoigt and Frank Thonfeld

**Based Social-Spatial Network Modelling Perspective 187**

The academic community, companies, and local and central administration all use digital data extensively. New and powerful technology solutions, including geographic informa‐ tion systems (GIS), have been evolving to enhance spatial data analysis. Spatial analysis is a type of analysis based on geographic exploratory techniques (data mining) and confirmato‐ ry facts and phenomena with space expression, contributing to policy issues and spatial planning. Currently, spatial analysis has acquired more importance than ever, due to the large volumes of spatial data (big data) available from different sources, such as social net‐ works and mobile phones. Moreover, one now has the opportunity to establish relationships and mathematical statistics in time and space (geosimulation).

Strictly speaking, spatial analysis can be seen as a way of analyzing spatial data, that is, turning data into information. In broad terms, it consists of enlightening and describing the processes and structures of spatial phenomena. It is the heart of GIS because it comprises the changes, uses, and procedures that can be applied to geographic data, adding value, expos‐ ing patterns and outliers that are not instantaneously recognizable, and creating new infor‐ mation that provides new insights and supports decisions. It embraces a diversity of data processing functions designed for deriving spatial relationships, patterns, and trends that are implicit in the data. The results could be used immediately on spatial problem solving and decision-making or as input for posterior spatial analysis and modeling. Eventually, spatial analysis is used for decision-making, to assist spatial planning and to support the creation of management policies.

Spatial modeling is an analytical procedure usually used in connection with a GIS in order to define the processes and properties of spatial features: it is a crucial technique of spatial analysis. Using models, within a GIS environment, for analyzing spatial data, one can accu‐ rately analyze the output data for an enhanced understanding by nonspecialists. Its spatial nature aids researchers in understanding the data and explaining the conclusions that are tough to express based on numeric and/or text data alone. The final goal of spatial modeling is to be able to simulate geographic phenomena in order to facilitate problem solving and/or planning. The information handling is done in numerous steps, each one of them represent‐ ing a phase in a complex analysis process.

This book aims to explain both Geographic Information Science and Spatial Science using a group of twelve unique research contributions that reflect some of the state-of-the-art ad‐ vances in spatial analysis and location modeling. The incorporated research goes from theo‐ retical to applied work, passing through some methodological insights. It is subdivided into three main parts: the first focuses on the research of Knowledge Discovery, Geographic Data Mining, Multi-Scale Representation and Spatial Analysis, the second studies aspects of, Spa‐ tial Modelling, System Dynamics and Geosimulation, and the third present's contributions in the field of Spatial Planning, Decision Making and the formulation of driving rules. This book is suitable for those that are interested in Geography, Geographic Information Science and Spatial Sciences.

#### **Jorge Rocha**

**Introduction**

**Section 1**

Institute of Geography and Spatial Planning University of Lisbon Lisbon, Portugal

#### **José António Tenedório**

Interdisciplinary Centre of Social Sciences (CICS.NOVA) Faculty of Social Sciences and Humanities (NOVA FCSH) Universidade NOVA de Lisboa Lisbon, Portugal

**Section 1**
