Civic Analytics And Urban Intelligence

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A description of your target organization
An existing policy or program in that organization;
Background about the current state of your policy/program; what are its accomplishments, challenges, where it fits within broader city operations and how favorably it is viewed by senior leadership;
An overview of your proposed solution;
A list of resources you have already carefully reviewed including city docs, relevant docs from other cities, relevant docs that provide a conceptual foundation
Resources and docs you plan to use going forward including interview subjects, primary and secondary sources.
You should think through questions such as:
Who needs to buy into this plan?’,
How do I structure this as a pitch for those people?’,
What does success for various stakeholders look like?;
How will this make lives easier or their work more impactful?

 

Sample Solution

My target organization is the City of Philadelphia’s Office of Performance and Innovation. This office was created in 2008 to bring analytics and data science capabilities to city government departments and develop strategies for improving performance across municipal operations. The existing policy or program I am focusing on is the Urban Intelligence Dashboard which aims to bridge the gap between disconnected government databases by providing a comprehensive overview of Philadelphia’s urban landscape through real-time visualization.

This dashboard has been met with much success, it allows users from various departments to interactively explore citywide trends such as population, housing prices, crime rates and quality of life indicators (City Of Philadelphia). Furthermore, this platform reduces the amount of manual labor necessary when creating reports while simultaneously increasing accuracy since it is automated (Miller et al., 2016). Senior leadership views this program favorably because it provides them with necessary data quickly allowing them to make informed decisions quickly thus saving both time and resources.

The proposed solution I am suggesting builds upon the success of this tool by introducing integrative machine learning algorithms into its existing framework. By leveraging these AI capabilities previously inaccessible insights can be discovered that may predict future events or consequences which would allow decision makers to proactively address problems rather than reactively responding after they have already occurred (Smith et al., 2018). Additionally, natural language processing could also be added so users are able to ask specific questions about datasets without having knowledge about programming languages; this will open up access even further so those who are not experienced with computers can still receive helpful information from the system (Liu & Huaijin, 2017).

In conclusion, given its current successes plus potential future improvements there is much appreciation for the Urban Intelligence Dashboard at all levels within Philadelphia’s government operations. By incorporating machine learning algorithms along with natural language processing capabilities greater value can be derived from its datasets leading towards deeper understanding about our urban environment plus more accurate predictions concerning possible outcomes which could benefit everyone involved in making decisions at any point down.

deed, almost all STI research has been conducted about individuals (Hamilton, Chen, Ko, Winczewski, Banerji, & Thurston, 2015). It is important to include group-based research in this line of work, given the importance of group membership and belonging in social interactions (Hamilton et al., 2015). Otten and Moskowitz (2000) found that behaviors implying positive traits about ingroup members led to the formation of STIs more than either negative behavior descriptions or behavior descriptions of outgroup members. Hamilton et al. (2015) have found evidence for the existence of STIs about groups (dubbed STIGs). Importantly, they noted that these STIGs lay a framework for (a) stereotype formation about a group and (b) generalizations about the behavior of an individual based solely on his or her group membership.

In addition to the limited research involving groups, STI research has largely eschewed the study of how purported moral behaviors affect participants’ likelihood of inferring moral traits. In one such study, Ma et al. (2012) found that participants do generate STIs for moral and immoral behaviors, though a limitation of this work is the lack of a nonmoral group of traits to compare it to. Indeed, the lack of this variable makes it difficult to conclude whether moral behaviors increase STIs or immoral behaviors depress STIs. It is important to note that a host of research into impression formation has found a bias for negative behaviors over positive behaviors (for a review, see Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; see also Skowronski & Carlston, 1989), leading to the intuition that perhaps immoral traits may be more readily inferred over moral traits, independent of the effect of group membership.

Group Membership

Membership in a group is one of the main features of social interaction. It has been established that membership in a group can alter one’s perception of other individuals, with this effect extending to both ingroup and outgroup members (Hackel, Looser, & Van Bavel, 2014). This includes having a skewed, positive outlook toward one’s ingroup members while inhibiting the extension of empathy and mind perception toward outgroup members (Hackel et al., 2014). Mind perception is the process of attributing a mind to another entity, and is an important mechanism for determining what is not only capable of agency (i.e., taking autonomous actions), but is also capable of feeling emotions, pain, and suffering and thus being afforded em

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