This week we studied at the GIS application on every level of the firefighting. First, GIS could be applied to municipal firefighting prevention and response by visualizing the geospatial information in an emergency. GIS can also be used to make a decision on the best location of a fire station. It can also work for a pre-incident survey to give respondents a better idea of what they will encounter when entering a building. Fire hydrant maintenance needs GIS tool as well to mark exact locations of fire hydrants. Besides, the emergency planning such as first responders and fire chief, and the wildfire management including planning and analysis and field operations are all related to GIS techniques.

For this week, we review the many roles that GIS has in crime analysis. GIS is used in crime analysis mainly for reducing the crime and improving the quality of life. In the beginning, we learned several crime theories. The routine activity theory argues that the socio-economic characteristics of people are not random across space; it is evident that routine activities are also not casual across the area. The rational choice theory suggests that most offenders make a rational decision to commit an offence. The criminal pattern theory comes up with an opinion that criminals usually commit crimes in certain areas. 

Then, we recanalize the crime as a geographical issue. Environmental criminology is a reflection that the geographic distribution of offences is not a random process. Since crimes are human phenomena, therefore, understating where and why crimes occur can improve attempts to fight crime. 

GIS is a powerful tool that can identify problem areas, produce maps for officers to use in the field, keep track of particular offenders, and assist in solving crimes. It often used in the crime analysis, which refers to the qualitative and quantitative study of crime and law enforcement information in combination with socio-demographic and spatial factors to apprehend criminals, prevent crime, reduce disorder, and evaluate organizational procedures. Crime analysis includes four levels of models. Intelligence and criminal investigative report is the study of “organized” or serial illegal activity. Tactical crime analysis is the study of recent and potential criminal activity. Strategic crime analysis is the study of crime and law enforcement information integrated with socio-economic and spatial factors to determine long-term patterns of movement. The administrative crime analysis is the interesting presentation findings of crime research and study based on legal, political and practical concerns to inform audiences within law enforcement administration, city government, and citizens.

Last week we learned what health geography is, and this week we discussed the GIS application in health geography. There are four major applications for GIS in the field: 

• Spatial epidemiology: spatial epidemiology is the study of the distribution and determinants of health-related states in populations and the application of the research to control health problems. There are four components: disease mapping, cluster detection, spatial exposure assessment and assessment of the risk of disease. Disease mapping describes patterns of disease and explores the spatial patterns. Cluster detection displays the bounded groups of occurrences. Exposure assessment is the study of human contact with chemical, physical, biological or social agents occurring in their environments. It examines the mechanisms and dynamics of events and how they relate to health outcomes. Methods available for exposure calculation include proximity,  interpolation, spatial modelling and satellite data.                                                   
• Environmental hazards: environmental hazard is a substance, a state or an event which has the potential to threaten the surrounding natural environment or adversely affect people’s health, including pollution and natural disaster. GIS applies in the field to identify the causal and mitigating factors.
• Modelling health services &  Identifying health inequalities: GIS can also be used in the two fields. One example is the Accessibility/Remoteness Index of Australia (ARIA). The GIS model allows the accessibility to any service to be calculated form all populated places in Australia. Another example is heart disease and healthcare accessibility.

This week we learned about what is health geography. The early health geography is related to medical geography. Medical geography is the application on geographical perspectives and methods to study of health, disease and health care. It uses the concepts and techniques of the discipline of geography in investigating health-related topics. It usually studies health, disease, and health care.

The contemporary health geography builds on the foundation of the previous medical geographers, but more focus on the alternative social and environmental perspective on health in which geography can play on the important part, along with other social sciences. Then, combining the humanistic geography and contemporary health geography, the incipient “post-medical” health geography is born. 

The early medical geography is a traditional perspective, which accepts disease as a naturally occurring, culture-free, and real entity. While the contemporary perspective holds an argument that the notions of health, disease and illness are problematic, and intimately linked to power relations in society. 

There are five strands of health geography:

1. Spatial patterning of disease and health 
2. Spatial patterning of service provision
3. Humanistic approaches to “medical geography”
4. Structuralist/materialist/critical approaches to “medical geography”
5. Cultural approaches to “medical geography”

This week we review statistics from the basics to multivariate statistics. Statistics help researchers to summarize, explore and predict the relations between variables. Basic summarizing tells researchers the general information the data expressed without complex in-depth statistical analysis. Summarizing data can be achieved through measures of central tendency, kurtosis, variability, skewness, relative position, and grouping analysis. However, if we want to measure the association between several variables, we should use more complex statistics, such as regression. 

Regression modelling examines the relations between dependent and independent variables. Linear regression is linear in coefficient; it is the simple regression. Ordinary least square (OLS) is a type of linear least squares for estimating the unknown parameter. It assumes the analysis is fitting a model of the linear relationship between one or more explanatory variables and a continuous or at least interval outcome variable that minimizes the sum of square errors. 

However, the OLS does not analyze the spatial correlation. The geography’s first law argues everything is related and near things are more connected to other things. So to consider the spatial autocorrelation, another regression modelling such as geographically weighted regression is needed by using the distance decay functions. 

Besides, generalized linear models (GLM) is a flexible linear regression of OLS that allows for response variable that has error distribution models other than a normal distribution. It is made up of three components: random, systematic and link function.  

Logistic regression provides an examination for a dichotomous response variable and numeric/categorical explanatory variables by calculating the binary probabilities. The multiple logistic regression extends to more than one predictor variables. It can solve limited dummy dependent variable in some research topics.

This week we learn about the importance of spatial analysis on understanding landscape ecology. We first review the definition of landscape ecology, which concerned with the mutual interaction between spatial pattern and ecological processes that occur on landscapes. Landscape pattern and process are two essential things in landscape ecology. The process can cause landscape patterns, in return, the patterns can modify processes. As spatial analysists, our goal is to examine the variability and the causations of variability among locations. 

The processes may happen under several conditions., a. Including abiotic conditions such as climate change and topography, b. Biotic factors such as competition, c. Human activity, d. Disturbance processes such as fires, volcanic eruptions, floods, and storms. The landscape pattern showing today is a reflection of multiple processes operating at different temporal and spatial scales.

Patterns only develop if the objects exhibit spatial autocorrelation. There are two types of spatial autocorrelation: a. First-Order process: the patterns emerge as a result of a response to an environmental factor. b. second-order process: if the patterns develop as a result of interactions between the objects themselves 

A process is considered stationary if the processes that govern the placement of an object do not change over space. a. First-order stationarity: if there is no variation in the intensity over space b. Second-Order stationarity: if there is no interaction between objects. Stationarity also assumes that the process does not exhibit a directional bias such as isotropic pattern, while the anisotropic patterns indicate the process does show a directional bias. 

In the end, we learned some vital landscape metrics:

1. Metrics of landscape composition: what is present for the objects themselves, without reference to where they are located (proportion occupied, relative richness, diversity and dominance, connectivity) 
2. Measures of spatial configuration: about the landscape and location (probabilities of adjacency, contagion, patch area, and perimeter, connectivity, proximity index, area-weighted average patch size)
3. Fractals; the spatial complexity

Scale

Scale refers to the spatial domain of the study, it could be either small or large, depending on the study purpose. Explanatory models are scale-dependent, the result can be significantly different from the scale changing. Small scale study is reasonable to ignore larger-scale variability. 

Spatial autocorrelation

Spatial autocorrelation which refers to the non-random distribution of objects has a spatial dimension. Objects are spatially autocorrelated either by themselves or by the environment. Moran’s I am a weighted product-moment correlation coefficient, where the weights reflect geographic proximity. Values of I over 0 indicate positive spatial autocorrelation and vice versa. However, sometimes the natural data is hard to determine the spatial autocorrelation for its natural irregulations. Smoothing functions are difficult to deal with the naturally-occurring random fluctuations, thus, kriging, the semi-variogram geostatistical method is used to model the irregulated natural data. 

MAUP

Modifiable Areal Unit Problem (MAUP) is a source of statistical bias that is endemic to all spatially aggregated data. It could be impacted by both scale and aggregation. The scale effect is the tendency, for different statistical results to be obtained from the same set of data when the information is grouped to varying levels of spatial resolution. The aggregation effect is the variability in statistical results obtained within a set of modifiable units as a function of the various ways these units can be grouped at a given scale. The MAUP can be a political issue such as gerrymandering. So, how to divide the spatial units is essential for spatial analysis. Commonly, spatial units can be divided into natural and artificial types. However, other elements may also impact the aggregated data. Simpson’s paradox argues if the values of the variables vary in correlation with another, then, it may be impossible to obtain a reliable estimate of the accurate correlation between two variables, 

Neighbourhood models

The geographical areas are made up not of random groupings but tend to be more alike within the area than those outsides of the area. The three main classes of neighbourhood models are grouping models, in which similar individuals choose to locate in the same area; group-dependent models, in which individuals in the same area are subject to similar external influences; feedback models, in which individuals interact with each other and influence each other.

Geographic Information Science (GIS) plays an essential role in managing spatial data and conducting spatial analysis. It helps spatial analyzer to answer spatial questions related to patterns, process, context, and optimization. In the course, we develop the awareness of the foundation of spatial data, understand the optimal approaches in the decision-making process for different research purpose, and visualize the spatial results. In the class, we explore the spatial analysis from three different perspectives: Landscape ecology, health geography, and crime analysis. 

Landscape ecology

Landscape ecology is the study of how landscape structure affects the abundance and distribution of organisms. The landscape structure includes abiotic patterns and processes such as soil type, lake chemistry, fire, and weather events, it also contains biotic patterns and processes such as the births, death, migration and species interaction. Rather than a traditional ecological approach in which the geography is removed from the analysis, landscape ecology emphasizes the effect of geography.

Health geography

Health geography studies the role of place, space, and community in shaping health outcomes and health care delivery. Disease ecology, health care delivery, and environment and health are three main themes in health geography. The disease ecology analyzes the spatial distribution of meteorological, biological as well as cultural phenomena and social, political and economic barriers that linked to infectious disease, Health care delivery includes the spatial patterns of health care provider and patient behaviors and tries to explain the inequalities and outcomes behind the spatial model. Environment and health is a focus on the impacts of environmental risks (i.e., contamination) on health outcomes. 

Crime analysis

Crime analysis focuses on the trend correlation of crime patterns to assist the operational and administrative personnel. It supports departmental functions including patrol development, special operations, tactical units, investigations, planning and research, crime prevention and administrative services. Environmental criminology is supported by criminological theories including routine activity theory, social disorganization theory, rational choice, and broken windows theory.