MACURISA2018

*New Jersey Roads and Addresses – Taking the Collaborative Route (Deepa Sanjeevaraya): As an initiative to support- geocoding, 911 Emergency dispatching, routing and transportation needs, the New Jersey Office of Geographic Information System (NJ OGIS) has created a statewide centerlines and addresses database. Which successively will also serve as foundation information for New Jersey’s Next Generation 911 database.
To attain these goals, the NJ OGIS is requesting local government partners (county, municipal and Public Safety Answering Point(PSAP) entities) to collaborate with us on reviewing, updating and validating the records to generate and maintain an accurate and encompassing database. This talk will present various collective methods available for potential partners for data maintenance and sharing.
**Detailed Statewide School Locations - The NJ Office of GIS Approach (Yelena Pikovskaya): Knowing the locations of schools throughout New Jersey is very important. They are needed for uses like proximity to known contaminated sites, emergency dispatching, homeland security, prospective homebuyers, Megan’s Law applications and drug free school zone development.
Every year the NJ Office of GIS (NJOGIS) works in conjunction with the NJ Department of Education (NJDOE) to update school locations throughout the state.  This process includes taking the NJDOE tabular data, running various models, analysis queries, research and manual verification using multiple sources to update the spatial location points of all the schools throughout the state.  The school locations include known public, public charter and non-public schools.  The annual updates include new school locations, relocated schools, closed school campuses and additional campus locations.
***Introduction to BIM - GIS Integration (Moderated Presentation ~25 minutes): Building Information Modeling or “BIM” technology has become the the hottest trend in the construction industry since Autodesk released their white paper “Building Information Modeling” in the early 2000’s. Promising improved planning, design, construction, and operations of the facility over its entire life cycle was what the Architecture, Engineering, and Construction (AEC) industry was looking for.
Similar to GIS, BIM is a digital representation of the different elements within a facility with the added value of performing advanced geospatial based analysis. BIM is the evolution of CAD towards GIS while focusing on its strengths for the AEC industry.
Last November, Esri’s president Jack Dangermond, and Autodesk’s president and CEO, Andrew Anagnost, announced “the start of a new relationship to build a bridge between BIM and GIS mapping technologies”. This unusual cooperation between the two toughest and largest competitors in the market ignited the spark for a new era: the GIS-BIM integration.
This presentation will focus on the BIM technology and its effect on the GIS technology and community. The presentation will present some of the challenges, opportunities and concerns in GIS-BIM integration through case studies. The presentation will conclude with a discussion on the future engagement of the GIS and BIM communities.
****CANCELED* Effects of Uber in Low-Income Communities (Moderated Presentation ~25 minutes): Transportation options have recently evolved into the sharing economy; however, not all communities have access to these options due to multiple historical and contemporary barriers. Is the sharing economy inaccessible to low-income communities? In the context of major transportation, hospitality, and marketplace branches evolving due to new
ventures, how will low-income communities adapt to changes in established public transportation modes?
The purpose of this study is to examine the effects of Uber on low-income communities. Specifically, the accessibility of services, or lack thereof, being provided to these communities and testing their feasibility as a mode of transportation in comparison with higher-income communities. Six randomly selected low-income and high-income census tracts will be used to explore the service coverage and accessibility of Uber. The data collected from low-income census tracts will be compared to those from high-income census tracts using a t-test and regressions to determine similarity or difference of service coverage between the tracts and the significance of different variables. Results are expected to display disparities between high-income and low-income census tracts. Low-income census tracts are predicted to have less service coverage and accessibility as determined by price and wait time. *CANCELED***