Blog 10: Crime and deviance

The Community Concept in Criminology: Toward a Social Network Approach

Leighton, B. (1988). The Community Concept in Criminology: Toward a Social Network Approach. Journal of Research in Crime and Delinquency, 25(4), 351-374.

In this study, The Community Concept in Criminology: Toward a Social Network Approach, Leighton looked at what the concept of a community is and how it is relevant with regards to crime and deviance. The word “community” has a different meaning to different people. He brings up a good point about how the idea of a community has declined over the years. “In urban areas, social ties characterized as being weak, communal ties as being scarce, and community as having decayed if not declined into oblivion.” (Leighton, 1988) This paper was written in 1988, which shows how the idea of community was starting to decline back then, it is still the same way today in 2018. The rise of technology advances has contributed to that.

The nodes in this study are the individuals in the community, the edges are the links between the node and their social ties. Leighton defined a network as, “a specific set of linkages among a defined set of persons with the additional property that the characteristics of these linkages as a whole may be used to interpret the social behavior of the persons involved.” (Leighton, 1988) Meaning he is looking at the links a person has to determine how their behavior is affected by those individuals in their social network.

He says that “network density is perhaps the most important structural attribute: it is the proportion of ties known to each other independent of the individual under focus.” (Leighton, 1988) The density of a network refers to the connections that are involved. Density is definitely one of the most important structural attributes in a social network, because it allows the person looking at the structure of the social network to determine which individuals are most important to the network. The individuals who are closer to the center of the network are going to be more important to the individuals who are at the outer periphery of the network.

The nodes who have a link to another node who has shown that they have deviant behavior is going to also have a higher chance that they also have an increase in deviant behavior. Most people have strong ties to individuals that they are closer with, have more in common with, or people that they trust more. “Ties linking them to other deviants relative to the proportion of non-deviants in their network would be predicted to be likely to become deviant.” (Leighton, 1988) This would indicate that communities can be looked at when determining deviant behavior and crime. If someone has a strong social network in a community and they have been responsible for deviant behavior, the individuals they have a strong tie with are also at risk to participate with deviant behavior also.

 

While this picture does not relate to either article I found about their studies, it does show a social network with regards to crime. It shows central nodes in the network, and their edges that connect them. I found this to be a good visual to show a close up of a social network.

https://hackernoon.com/fight-crime-with-social-network-analysis-7a879d4a65ea

Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago

Bastomski, Brazil, & Papachristos. (2017). Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago. Social Networks, 51, 23-39.

In this study, Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago, they were looking at whether neighborhoods and communities contributed to the crime rate in the city. They wanted to determine if individuals who live in the same neighborhoods have a similar likelihood of crime. “We constructed a co-offending network using arrest data from the Chicago Police Department, where nodes represent unique individuals arrested by the police during this time period and each edge connecting the nodes represents an instance of co-offending.” (Bastomski, Brazil & Papachristos, 2017) In this study, they got their network by going to a Police Department and looking at individuals who had been arrested in Chicago, these individuals were each the nodes. They then looked at the people they may have had a connection to in their network, these individuals were the edges because they had a link to them.

Crime rates are more likely to be higher in neighborhoods where individuals may not have a high degree of people that they know (or edges) but it is more likely to happen in a neighborhood where they have a smaller amount of edges, but they are closer friends. “A neighborhood with a high degree and low embeddedness reflects ties that are potentially more vulnerable to disruption; whereas a neighborhood with a low degree and high embeddedness reflects a small cluster of highly inter-connected neighborhoods.” (Bastomski, Brazil & Papachristos, 2017) This means that neighborhoods who have individuals that are closer together, this could be a small town, that they are more likely to be a closer-knit community. This does not necessarily mean that crime rates are going to be lower, it just means that one node is going to have fewer edges, which could indicate a stronger friendship to the edges that they do have.

If a person is hanging out with a person who has been to jail before for a crime, and they are with someone who is a bad influence, they are more likely to be influenced by deviant behavior. “Employing co-offending network data presents several strategic advantages. First, criminological work has established that co-offending acts account for a substantial proportion of all crime.” (Bastomski, Brazil & Papachristos, 2017) Most individuals are going to carry out a crime with someone that they have a strong tie to, because that person is most likely someone that they can trust. The study talked about how a social network does contribute to the spread of violence in a neighborhood. A way that law enforcement can try to reduce crime is to try and come up with “an empirical approach for identifying neighborhoods that require the most assistance and intervention.” (Bastomski, Brazil & Papachristos, 2017) The focus would be on those neighborhoods that have a high level of crime and a high arrest rate of those individuals. Focusing on those individuals and finding out exactly what led them to deviant behavior is going to be one of the best ways to address crime rates. It is also going to help with intervention and finding out what more can be done in a neighborhood where individuals have a close social network.

Being able to use social network analysis is huge when it comes to looking at crime and deviance. When using social network analysis, we are able to look at what people are most important in a network, along with their closest friends or family, the people they interact with on a regular basis. We are also able to determine if a person’s close ties do have anything to do with the chance they will have a higher chance of deviance. Using social network analysis will continue to be important when looking at different social networks.

 

Blog 7: Habermas and Castells

The Public Sphere is declining, “If we are to believe what sociologists are telling us, the public sphere is in a near terminal state.” (Johnson, 2006) Habermas defined the Public Sphere as both the public and the state working together and communicating with each other. A democratic society is a good example of a public sphere because the public are able to have a say in what they what. People with similar and different ideas are able to come together and talk about what they want. The Public Sphere is “a willingness to engage with the particular issues thrown up by contemporary politics is, for Habermas, a central responsibility of the critical theorist.” (Johnson, 2006) Meaning it is good to engage with each other about different issues.

“An” Understanding of Habermas and the Public Sphere

Castells defined a network society a little differently, he defines it as more of an online way of communicating instead of in person. “Castells defines ‘network’ explicitly as a set of interconnected nodes of which he mentions such examples as stock exchange markets and their ancillary centers of advanced financial services in the network of global financial flows.” (Anttiroiko, 2015) Nodes are important and are connected to each other in different ways.

 

 

This is a good image that shows how public opinion can be influenced by media.

https://www.researchgate.net/figure/Triangular-Model-of-the-Public-Sphere-of-Science-and-Technology-NOTE-The-triangular_fig1_249677889

Having a network society can be both a good thing and a bad thing. While online communication can be good, it also takes away from in person relationships. It is also a problem when some people may not have internet, so it hasn’t really improved their lives any. When it comes to education, we have seen over the years that learning has focused more on technology. Today, kids take laptops with them to class to take notes on, whereas before that wasn’t an option. Also, in health care, a patient’s health file used to be stored with lots of other files, but we are seeing more and more that even with health care, those files are now online. Technology is always changing and everywhere we look, there are always new advances when it comes to technology.

Anttiroiko, A. (2015, July 15). Networks in Manuel Castells’ theory of the network society. Retrieved from https://mpra.ub.uni-muenchen.de/65617/

Johnson, P. (2006). Habermas: Rescuing the Public Sphere. London: Routledge. Retrieved from http://proxy.library.vcu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,url,cookie,uid&db=nlebk&AN=157805&site=ehost-live&scope=site

Blog 5 node centrality

When it comes to social networks, there are four main node centrality measures that describe networks. Node centrality measures are important in social network analysis, but also in other fields when doing research. Degrees look at how many links a node has to other nodes in a network. Betweenness centrality looks at the shortest path between nodes. Closeness centrality refers to how close a node is to other nodes in a network, and eigenvector centrality looks at how important a node is, it looks at how many other links to other nodes in a network there are. If a node has a high eigenvector centrality, it is well connected to other nodes, it is important.

 

This is a good example of how people are the nodes, it shows the links between all of the nodes in this particular network. Some are closer together than others.

 

http://sites.tufts.edu/greatdiseases/2015/03/23/news-views-wireless-sensors-help-scientists-map-staph-spread-inside-hospital/

Node centrality measures are not only used when looking at social networks, they can be used in the health field also when looking at infectious diseases and how they spread. “In epidemiology, some possibly infective contacts between individuals are long term (friends, family) but many are fleeting (people in the street or the market place).”(Hyoungshick & Anderson, 2012) When it comes to infectious diseases, it is important to find the node that had the most contact with others, they may be long term and people they have been in close contact with over a period of time, or they may be short term, If someone is sick on an airplane, and they touch their tray, then it doesn’t get cleaned completely, the next person who sits there could get sick. They didn’t have a long-term connection to each other, in fact, they didn’t really have a connection at all, apart from the airplane tray. In the article by (Hyoungshick & Anderson, 2012), they looked at merchants and villages and the closeness and betweenness values between them to look at how diseases spread.

This picture shows an initially infected adult and the different links it has to other nodes(people). It is a good way to show how diseases can spread. http://www.homelandsecuritynewswire.com/dr20150831-new-research-aims-to-slow-the-spread-of-infectious-diseases

In cases of Ebola or STD’S it is important that they find who came into contact with the person who is sick. The same goes for vaccinations and how they protect against disease. “If a node has twice the neighbors of another, it has twice as many nodes to which to spread an infection.” (Holme, 2017) Meaning they not only have twice the way to spread an infection, but also get an infection. If someone has a close degree centrality and they are sick, it means that they are looking at all of the links one person has to another (who they have come in contact with) When looking at closeness centrality, this looks at how close a person is to other people. Betweenness centrality would look at the shortest path between two people, and eigenvector centrality looks at how important one person is in the network and whether they had come into contact with a lot of people. If they had, this means they are a significant person, they might be a carrier of the disease. This shows that node centrality is important, not just with social networks, but even when health professionals are looking at the spread of disease.

Holme, P. (2017). Three faces of node importance in network epidemiology: Exact results for small graphs. Physical Review E, 96(6). doi:10.1103/physreve.96.062305

Hyoungshick, K., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E,85(2). doi:10.1103/physreve.85.026107

Blog 2: Social networks

Social network analysis is important in modern society because social networks are used every day. An example would be Facebook or Twitter. “Facebook where the links indicate friends or links, or Twitter where the links may be retweets or followers.” (Yang & Keller, 2017) Social network analysis is important because we form relationships and have relationships, whether that be with family or friends or meeting new people and starting new relationships with people. Social network analysis looks at relationships between actors, which are also known as the nodes, and the ties, which are the edges, between them. “Social network research can be seen as one approach to dealing with a central problem in social theory, which is to capture the relationship between the individual and society.” (Keim, 2011)

This shows how many social networks there are out there. I thought this picture did a good job of showing the different types of social networks, whether it be social media, search engines, communication apps, etc. https://www.mirnabard.com/2010/02/15-categories-of-social-media/

When it comes to social networks, there can be positive and negative relationships or strong and weak ties. I didn’t realize that social network analysis also included negative relationships. When I hear the word social networks, I always thought of positive relationships. My thought process was one sided because I mainly thought of social network analysis having to do with the internet, not people.

 This shows how strong ties and weak ties work. It shows the individual and how they have a few close people to them which are the strong ties, then the people on the outside are the weaker ties. https://howardogawa.wordpress.com/2012/01/27/weak-connections-are-your-strongest-ally/

Social science looks more at an actual sample of what is being studied, it deals more with math, such as statistics or graphs. Traditional social science research is linear. “Social science involves social entities involved in social action.” (Robins, 2015) Relational data looks at different connections and ties to people in social networks, what connects one actor to another. When I think of relational, I think an example would be coworkers. The connection between them is that they work together. Descriptive analysis goes more in depth about the research, it can look at what makes networks the same or whether they have differences. It would look at the types of actors involved and how relationships are formed. Predictive analysis takes what has already been learned and takes data that has been collected and it looks at predictions about what might happen. This doesn’t always work for all research, it depends on what is being looked at. While a lot has been learned about social network analysis, there is still a lot to learn.

Keim, S. (2011). ‘The Social Network Perspective.” in Social Networks and Family Formation Processes.

Robins, G. (2015). Doing social network research: Network-based research design for social scientists. Los Angeles: Sage Publications.

Yang, S., Zhang, L., & Keller, F. B. (2017). Social network analysis: Methods and examples. Los Angeles: Sage.

Linked Barabasi

Barabasi talks about how networks are found everywhere. I didn’t realize that networks could be found in math and science, that is talked about in this book. I had an understanding that networks were mainly related to computers, but what this book is trying to say, is that networks are more than that. The use of nodes and links are used when it comes to talking about networks. An example would be a computer, which has wires, the wires are the links. When we go on the internet, that is an example of a connection. The internet is a way to get information out easily and spread to many people at once.

This picture shows nodes and links in a social network.

https://www.projectrhea.org/rhea/index.php/Walther_MA279_Spring2016_topic4

Barabasi looked at Euler’s work on graph theory, which allowed nodes to be shown on a bell curve or normal curve, just like we would see with graphs in math. He also found that hubs are where nodes are connected.

 

This picture is a great representation of social networks. It shows the nodes and links. https://www.slideshare.net/8of12/social-network-analysis-for-competitive-intelligence

Barabasi also mentioned the six degrees of separation. Meaning, chances are if we know six people that we will most likely know another six people that they know. This means that the people are the nodes and when they interact with each other, that connection between them becomes a link. I am not sure I completely agree with this. Since there are so many networks that one person can be a member of, it is unlikely that they will know people from outside social networks that they don’t regularly interact with. For example, if I know six people from work, chances are, I won’t know six other people that they know outside of work. While networks are everywhere, I don’t think networks are that small.

I did find it interesting how networks are found practically everywhere. Where I do agree with Barabasi, is when he talks about how technology will continue to improve. Even from the time this book was written to now, there have been many advances that have been seen over the years, this will continue to happen.