I held various student and staff positions at the former Division of Government Research (DGR), University of New Mexico (UNM) until my retirement as a Senior Research Scientist 1 during February 2012 after more than thirty years including several years as a graduate student. This retirement was primarily due to a reorganization which resulted in the elimination of DGR that had served as a component of UNM's public and community service mission since 1946. Although DGR staff had requested an open hiring process for a new director and continued separate departmental status, an appointment was made instead that enabled this reorganization. All of DGR's long-time senior staff declined to become part of the newly created Geospatial and Population Studies (GPS) given the details of this reorganization. I now have time to focus on continuing my education and conducting various research projects, some of which were begun during my time as a graduate student and others while employed at DGR in the past.
I am a applied geographer ( location theory - retail geography and spatial statistics ) with additional focus on urban, transportation, and healthcare applications. My previous work experience and continuing education have provided a strong background as a GIS (Geographic Information System) professional and also as an improving geospatial data analyst. I completed a M.A. in Geography and a B.A in Anthropology/Archeology with an undergraduate minor in Geography at UNM. I did transfer to UNM as a junior after my first two years at Franklin Pierce College in New Hampshire. I also attended one semester as an undergraduate at Southern Connecticut State University where I completed several additional geography classes.
Anthropology and archeology were the primary reason I came to UNM, however, the transition to geographer was easy as I appreciate the theory and methodology of geography and also the contemporary applied focus. The study of anthropology provided a good foundation in the social sciences and methodological developments in geography have provided the technical and analytical tools by which I can continue the application of my knowledge. Fortunately, I have had a great opportunity to do this with the mostly public and community service oriented projects conducted by DGR.
As a full-time UNM staff employee and current retiree I have used my tuition benefit to take additional classes that have been useful for my ongoing research projects. I completed a minor in statistics even retaking several classes I had many years ago. I am continuing to take additional mathematics and statistics classes (recently completed Stat 579 - SAS Programming). I have also taken several geography ( GIS/GI Science) classes to keep up with current developments (recently completed Geog 525-Advanced GIScience Seminar, Geog 588L-GIS Concepts and Techniques, Geog 580L-Quant. Methods, Geog 580L-Spatial Statistics using R, Geog 586L Applications of GIS, Geog 587L-Spatial Analysis and Modelling, Geog 499-Python Programming, Geog 528-Advanced Programming for GIS using python, plus multiple Geog 591-Problems). Most of these classes were focused on spatial analysis and software development. These are classes that were not available when I was previously a graduate student. Unfortunately the UNM Geography and Environmental Studies Department does not offer an undergraduate or graduate GIS certificate that would be very useful for working professionals and non-geography graduates like those that have become popular at many other universities (for some examples see: ASU, UA, SDSU, DU, PSU, and UCONN ). Regardless, I think I have already completed more than the equivalent with similar graduate level geography classes taken at UNM. Hopefully, UNM will eventually establish a graduate GIS certificate similar to those at many other universities that would provide the opportunity for many former graduates and others to update their technical skills while working as GIS and related professionals throughout the state.
In addition, I have completed many ESRI (Environmental Systems Research Institute) Virtual Campus and Instructor led GIS training classes (recently completed the Spatial Analysis MOCC, the Location Advantage MOCC , and the Geo Apps MOCC ). I have worked through several Penn State Online Geospatial Education Classes which can be accessed freely (as a non-credit resource) under their Open Educational Resource Initiative. The Esri - YouTube Channel is another excellent resource with many helpful presentations and technical sessions from the users and developers conferences that I have used extensively to keep up with recent developments. An affordable source for many technical pubications is Packt that I have found very helpful given the rapid developments of GIScience and related technologies.
I was also a long-time member of the Urban and Regional Information Association (URISA through 2015) and earned my Geographic Information Systems Professional (GISP) certification from 2005 to 2020. I have been a long-time member of the New Mexico Geographic Information Council (NMGIC) and have served as an elected board member. I had regularly attended the New Mexico Geospatial Advisory Committee (GAC) for many years representing DGR until my retirement from UNM. I also received the NMGIC Lifetime Acheivement Reward in 2019 (see 2019 Map Legend), which was a surprise as I don't consider myself to be completely retired yet.
My current studies and research projects are intended to update and improve my skills. As new programs continue to be developed at UNM and NMSU (see Joint Ph.D. Program) I have continued taking some classes (completed Geog 601 and many Geog 691 - independent studies) as a non-degree graduate student. I am currently taking some online classes at UNM's College of Public Health as my current research is related to these subjects. My plan is to only work part-time in the future, but would consider another full-time job again if it were necessary. I hope to stay involved doing interesting applied geography, GIS, and related statistical (spatial data analysis) work using my improving analytical and computing skills.
I have been involved with GIS development and application for many years in New Mexico. During this time I have noticed that much of work in the state has been primarily operational, focused upon systems development, data collection, and routine data description. The primary tasks are mostly building a GIS and related databases along with making maps, tables, and charts based on elementary queries. Most recent work seems dedicated to the preparation of even more elaborate and colorful, but mostly descriptive data oriented web pages, some with a focus on data download (the "firehose or exhaust pipe approach" - dumping raw data to the web). There have been very few examples of data integration and analysis. The combining of data using spatial and statistical methods to produce more useful information related to specific research questions (see ESRI's ArcGIS Spatial Statistics Resources and The Importance of Where). However, recent Open Data initiatives are clearly making government data more accessible and have promoted more analytical applications.
DGR had a long history of reliance upon the innovative combination of both SAS and ESRI products to conduct a wide variety of applied data integration, data analysis, and GIS projects (see GIS at DGR powerpoint). During my time as a graduate student and as staff at DGR there were several data analysis oriented research projects combined with GIS that were developed with other former staff and students. These projects are still potentially very useful and are worthy of my current efforts to continue and to build upon.
DGR was never exclusively focused on transportation and traffic safety research although a large part of our contract funding for many years was related to these activities. To better focus on traffic safety problem location identification it was necessary to develop in-house GIS capabilities before some commercial products were available. Most notable was the early development of a SAS based GIS (GRNDB - Geographic Road Network Database) used for the mapping and analysis of traffic crash locations (linear referenced and georeferenced to intersections). Recent versions of ESRI's ArcGIS now allow for more well-developed traffic crash analysis and spatial statistical analyses in combination with Python scripting. It is now possible to evaluate if some previously developed SAS based procedures used for traffic crash analysis and other applications such as access to health-care providers and facilities can be revised using recent ESRI tools and also R plus other open source statistical software. However, in certain circumstances, it may be difficult to match the efficiency provided by combining the data manipulation power of SAS with the GIS functions of ArcGIS as previously developed at DGR.
I will be working on these research projects more in the future as I now have time to do this. It is important to note that these are currently unfunded projects that I feel are very important to continue in the spirit of public and community service. Most of the data are available to the public or can be derived from several non-commercial sources. I intend to use my expertise and skills to compile the data and to perform the analyses. Once the technical developments using updated GIS and statistical facilities are completed, hopefully more recent data maintained by state government agencies can be obtained. I intend to work with other researchers to interpret the results and to make some conference presentations and prepare publications.
I am currently using a combination of personal computing facilities consisting of open source, student, home-use, and non-commercial versions of software to conduct this unfunded research. If some funding becomes available in the future, I will purchase additional required commercial versions and also use academic versions acceptable for university research purposes.
Previous Developments: I used ArcGIS ModelBuilder for both my Albuquerque Food Store Location Analysis and the New Mexico Health Care Providers research projects. Both gravity models were developed with ArcGIS ModelBuilder and maps have been produced in ArcMap depicting the results. I also prepared ArcGIS Python scripts for both of these gravity models using ArcGIS(ArcPy). Given some minor problems that still need to be resolved with ArcGIS (mostly automating symbology) I also used QGIS and Python for the health care gravity model. I have developed a Python (GDAL/OGR) script that reads a shapefile, calculates the gravity model, and writes a new shapefile with original data and results. In addition, I prepared a QGIS plugin based on this Python script. More developments were completed using Ubuntu-Linux and other open source geospatial software. Additional versions of the retail and healthcare gravity models were developed using R and Python-GeoPandas. I have also explored the infogroup data previously obtained by the NM Department of Information Technology and provided by the NM Community Data Collaborative as a source for more up-to-date retail and healthcare data for use with both gravity models. I have also prepared some examples of other spatial analysis applications using public data for New Mexico available from other web sites. I used both UNM's GPS and Esri Demographics as the source for population data estimates at the census tract and also the block group levels of geography. There are some differences at the census tract level between both of these estimates (see ArcGIS Online Web Map Application - NM 2015 Census Tract Population Estimates) and only Esri currently provides estimates for block groups. See the links below for more details about these developments.
Recent Updates: I have started to statistically compare results from the healthcare gravity model previously developed by DGR, UNM with the more recently developed family of two-step floating catchment area (2SFCA) methods. I have previously developed these methods using SAS and Python-GeoPandas. As Esri continues to improve the integration of ArcGIS with open source statistical packages (see Esri -YouTube and specifically 2021 Developer Summit Tech Sessions) I am continuing to develop updated R and Python scripts plus Jupyter Notebooks that better illustrate these methods for other researchers to use and improve (see Python in ArcGIS Pro). These new developments will primarily use the population data provided by Esri's GeoEnrichment services that are part of the ArcGIS API for Python. Because Albuquerque has an elevated level of property related crimes and significant traffic safety issues I will continue to demonstrate how spatial (statistical) analysis methods plus Geographic Artificial Intelligence ( GeoAI) can be used to identify problem locations. See the links below for more details about these developments.
Long Version, 2020
Larry Spear, Sr. Research Scientist (Ret.)
Division of Government Research
Non-degree Graduate Student
University of New Mexico
Email: lspear@unm.edu lspearnm@gmail.com
WWW: https://www.unm.edu/~lspear
LinkedIn https://www.linkedin.com/in/larry-spear-93371970
UNM's Home Page
Last Revised: 9/5/2024 Larry Spear (lspear@unm.edu)