Hello all, my name is Jeremiah Nieves. Thanks for taking the time to hear a bit about myself and my current and future research interests that will broadly encapsulate with the title big world, little people, human environments from above.

Over the next 20 minutes, I'll introduce myself a bit as a quantitative human. Geographer are professional in remote sensing and in Edgecator.

Hopefully I'll also draw the connections as to why myself my work would be a great fit for the geography and planning Department at the University of Liverpool and vice versa.

So bit about myself, I'm currently a postdoctoral research associate at the population center at the University of Colorado, Boulder.

Before that, I spent six years as a researcher with the world population modeling group at the University of Southampton where I led the urban modeling research, managed work packages on a multi $1,000,000 grant, let him manage the team producing high volumes of data, design GIS, infrastructure, negotiated grant proposals with stakeholders and developed and LED paid methods workshops.

All of this is led to me having a strong publication record and much more experience as an applied researcher, despite having been awarded my PhD in September.

Additionally, over the past 10 years I've been University educator in remote sensing, mathematics, geography and GIS, where I've had the roles. Of course, designer Majuli guest lecturer in Demonstrator.

As I mentioned, I'm a human geographer that focuses on the use of remote sensing and statistical modeling methods across space and time.

For those who are unfamiliar, remote sensing is the remote measurement of electromagnetic radiation, such as light and heat, that is reflected from an object.

Here I'm referring to the reflections from the earth surface and referring to sensors that are on satellites. Airplanes in unmanned aerial vehicles or drones.

My broad research themes include the urban and rural continuum, population and settlement dynamics, spatiotemporal modeling interfaces of humans, and the environment, public health and associate conomique, inequality's, and historical geographies.

I'm always happiest working within their disciplinary group as there are a few problems that I've found are best tackled from a single direction.

I'm a strong believer that if you want to go fast, go alone, but more importantly that if you go together, you go far.

But why the combination of remote sensing and human geography? My feelings on this is most things are better worded by someone else.

Astronaut Edgar Mitchell, upon seeing the earth from the Moon, said you develop an instant global consciousness of people orientation and intense dissatisfaction with the state of the world in a compulsion to do something about it. From out there on the Moon, International politics looks so petty.

There really is something powerful about visually putting ourselves in our environment within a larger context that readily compels attention, whether from expert politician or layperson.

Remote sensing also provides repeated measurements of the natural and built environment at spatial and temporal scales that most researchers can only dream up.

For instance, a single Landsat 8 scene contains over 64 1/2 million pixels each represent a 30 metre square section of the Earth.

And that satellite resembles those same locations once every 16 days.

Remote sensing is applicable across a variety of disciplines and topics and is particularly powerful when grounded with mixed message methods to better understand human environment dynamics, relationships, and experiences.

Because of the consistency of remote sensing and the large amount of data available across the Globe and across time, the research potentials are vast and because of the Accessibility of their visual format, remote sensing, imagery and data are readily interpretable by a variety of audiences, which increases the potential for impact.

Recently my research looked at a key human environment interface, built areas of settlement, or humans reside in work. I let this work between 2016 and 2020 and it is already met. Several ref three and four star criteria.

Here I created a built segment growth model that accurately predicts annual global human settlement expansion between 2000 to 2020 at 100 meter resolution.

This was a significant advance in the field is this was the first globally applicable and consistent approach for settlement modeling that allowed for subnational variation of populations to drive the models. Further, this modeling framework sets the foundation for long-term settlement predictions both into the future and into the past, as well as addressing larger questions on settlement population dynamics.

This model data was adopted to inform the models producing the annual global population products of the World Pop Research Group. My modeled settlement extends were the key driver of the population models from 2015 to 2020. As our last observed data at the time of production in 2018 was for 2014.

The 2020 modeled settlement in population data now serve as the underlying population data in the COVID-19 modeling efforts by Imperial College London and the Institute for health metrics and evaluation, which are informing national and global responses and policies to ongoing pandemic.

These data have also been adopted by the United Nations population fund and other public health NGO's and has been featured in the closing remarks of international conferences and forums.

Currently, I have several research projects underway while I'm at the University of Colorado Boulder that spanned both the USA and Europe.

I'm working as a part of a larger team investigating the use of built environment and demographic data in assessing vulnerability and environmental risk from natural hazards.

I've developed several research questions and I'm leaving work looking at historical ships in the built environment, population, and demographic compositions following what used to be one in 1000 year disasters. Specifically, I'm interested in spatial temporal change and associate conomique inequities that may have occurred around these events. Additionally, I'm investigating the changing landscapes of flood risk and vulnerability in small towns of Apple Acha.

Lastly, I'm also involved in projects looking at remote sensing based measures of inequality across space and time. The impacts of conflict on fertility.

So while I focus on remote sensing applications and spatiotemporal modeling, I'm a bit topic agnostic. As long as as humans in space involved. I find this keeps me challenged and interested and I'm always looking for new collaborations regardless of discipline and topic. However, I am currently most interested in answering questions about where, when, how much, and what kinds of human settlement exists across the globe, how we can model these settlements, the applied use of such urban in settlement data, and the interfaces between human health the environment.

And so she economic structures.

Accordingly, now focus on the themes of settlement and urbanization. Modeling signals and echoes of settlements and environment health and vulnerability. These slides will follow a layout with some identified potential funders and grants on the right side of the slide.

Remote sensing data on settlements are becoming increasingly available at high spatial resolutions and across more time points, but methods to predict future settlement growth have not kept pace with advances in data.

So the question becomes, how can we predict settlement demand in globally consistent, incomparable ways?

Bayesian probabilistic approaches can build upon some of my previous work and can not only provide point estimates a settlement growth, but provide intuitive uncertainty estimates.

This will provide comperable and robust predictions that can be used at local to international scales while being comperable across political boundaries.

And such methods will begin to address larger questions of population and settlement dynamics, as well as trends in Lane cover. Use overtime and these data produced would have wide appeal for applications. I would aim to leverage my existing network to utilize such data sets and promote adoption of more robust and modern methods of settlement estimates.

Similarly of these new global settlement data sets, there are many types with varying degrees of dramatic overlap in various definitions of settlement. Knowing where and what settlement truly is impacts population modeling and population applications.

However, methods of using these data are limited, not accessible to many practitioners, and have high validation data requirements. I'll address these issues using advances in machine learning methods to statistically combine similar data sets to provide estimates of settlement location and magnitudes, as well as derive new settlement typology's.

Additionally, I'll provide these tool set to be in an interface or dashboard, along with tutorials broadening the user base to non experts and non programmers as well as opening potential for capacity building and engagement funds.

Such a tool, set in the drive data products, would provide a more complete picture of the human footprint on Earth and allow for purpose built settlement fusions to be created by users. This is a vast improvement over current data sets which provide little to no information on the uncertainty of their estimates.

This work would address what are the actual human footprints on earth and how different measures of the built environment can be combined to better inform our concepts of the human settlement fabric.

Detecting settlements in their widely varying forms using remote sensing alone remains a challenge, even with a broad Ledger on how various settlements appear once built.

However, there is little literature on the spectral shifts that occur when laying covered transitions from non settlement to settlement.

Additionally, one would logically expect that above average population movement and proceed any new and expanding areas of settlements idea to address the detection challenge would be twofold.

First I will be.

Examining common spectral remote sensing signals and population movement signals that precede and follow new settlement creation.

Here, in addition to remote sensing imagery, I can leverage anonymized Google location history data from cell phones to develop time specific movement surfaces.

Second or the echoes?

Here I would leverage historical tax and demographic data to better understanding quantified past population settlement dynamics.

I utilized trends from this data to better inform models of past and future settlement Genesis as well as understand population settlement dynamics.

Together, the signals and echoes can be combined to refine the image search algorithms to increase the efficiency of such data productions as well as reduce the lag time between imagery collection in settlement data production.

Discovering these signals and echoes of past population settlement dynamics will provide greater understanding on how settlements originate and develop, as well as provide greater predictive power and efficiency and updating settlement footprints from imagery in inherently scalable way.

Now the section is low, less detailed as these are areas that I'm highly interested in investigating, but we need to assemble a larger team with various skills and background to put forward proposals. However, the potential impacts are great.

Currently remote sensing detection of ground level, aerosoles or limited in course in scale. However, improvements in statistical methods and Simulation modeling provide us with opportunities to better relate these measurements to ground level sensors.

Additionally, existing ground monitors of air pollution are known to lack spatial specificity, particularly within urbanized areas.

However, advancements in technology make particulate measurement devices much more affordable. This provides the opportunity to deploy relatively low cost meso Nets of sensors across urban areas with active data collection via wireless communications.

Combining this refined device location data from cell phones.

Estimates of interurbain particular levels and exposure, or more accessible than ever.

Additionally, similar work has been done with gunshot detection devices, allowing us to optimize sensor placement for such a massive net via modeling and quantify the gaps in existing monitoring networks.

Based upon my time in England and exclusively riding public transit while living in varying areas of socionomic averages, I began to question the equity of such transport services across socio economic gradients.

Such questions revolve around access to payers, quality of transport, service, distribution of commuting, wait times, as well as the resiliency and redundancy of services across space and time. Some of the data that consumer data Research Center, as well as openly available data would be able to address these questions, but overall a mixed methods approach would be preferred.

Additional questions regarding services before and after large socioeconomic events such as the ongoing pandemic or the 2008 recession would also be of interest to explore.

Similar questions regarding transport Accessibility for current and future aging populations would also be of importance and wider interest.

This approach will be testable at the local scale and be scalable up to the national level as well, with findings including policy recommendations to better address transport.

Also, during my time in England, I've noted just how unsuited the physical infrastructure is for dealing with high heat events. Some of y'all may remember 2018 and how that heat wave just didn't quit with London predicted to have a climate similar to Barcelona by 2050. Understanding the intersection between physical infrastructure, social demographic and environmental factors and how they contribute to vulnerability or resiliency is more important than ever.

Their approach will be a mixed methods combining geospatial data and housing stock records with interview and survey components to triangulate in on the contribution of the structural environment to vulnerability in resiliency. In the face of high heat events.

This would be scalable from local to national levels, with the idea being to create standardized frameworks assessments, incomparable data sets that could be scaled up from local to national levels, and develop policy advice based upon the findings. Broader impact to be achieved through interactive portals, allowing for greater citizen engagement, understanding and ownership of the final.

All of my proposed research directions would greatly benefit from the collaboration of folks within the Department and or be complementary. Dawn going research. The unique combination of geography and urban planning within a single Department provides rich opportunities for translating spatial data. No location. Bespoke policy recommendations as well as assessment of policy impacts of various scales, in particular, the geographic data science lab would provide an excellent collaborative environment on approaches and methods, as well as the infrastructure and communities for disseminating data and findings.

While maximizing the impact of such work, other resources such as a consumer research data center provide a whole host of opportunities outside of these research directions, which I'm equally interested in exploring. Lastly, there's a plethora of opportunities for collaboration with other departments in the University in fields such as public health, ecology, and archaeology to name it.

While I'm passionate about doing replicable and open science with the exponentially increasing amount of data we find ourselves with, I'm just as passionate about teaching and mentorship. I find that excellent programs of teaching and mentorship lay the foundation for a vibrant research program within a Department.

I have a particularly strong interest in teaching remote sensing courses at both the undergraduate and postgraduate levels. I'd set up these as an introductory remote sensing course and in advanced remote sensing course that can educate undergraduate and postgraduate students simultaneously.

In the introductory course I would familiarize students with the various types of remote sensing imagery, their conceptual underpinnings, and the skills necessary to source and utilize remote sensing data for common operations within both industry standard and open source software.

Lab based practicals and a potential locally based field data collection component would be key to this course.

For the advanced course, I would guide students through more specialty imagery types, image processing techniques in time series methods.

I would focus the advanced course around one or two applied projects that require students to combine remote sensing imagery with other data. Carry out a brief analysis and properly communicate findings while still guided. There will be some flexibility in the topics allowing the students to better guide their own remote sensing skills. Specialization based upon their study interests.

In this scenario, there will be additional criteria for the post graduate learners to meet within the courses with a heavier emphasis on programmatic image analysis. However, demand was high enough, there would be more than enough material for separate undergraduate and postgraduate courses.

Or for more specialty remote sensing topics to be covered as separate modules. These remote sensing courses will be of interest to many degrees offered within the Department and programs across the University.

With remote sensing being so broadly applicable, I see this as providing numerous opportunities for guest lecturing across various modules, as well as introducing data collection to existing field courses or introducing entirely new field course focus on instead to remote sensing data collection.

As I briefly mentioned, there is quite a bit of potential for seminar and lambaste modules that delve deeper into specialty topics such as remote sensing is related to populations in the built environment, environmental monitoring using remote sensing, and high volume programmatic image analysis.

There are also areas of special remote sensing imagery such as radar based hyperspectral imagery and others that can be developed into their own modules. Do the depth of the subfields both conceptually and methodologically.

Of course I have other areas that would be interested in teaching as well.

Some of these topics would include a seminar based module and applications and ethics of algorithms, Bayesian spatio, temporal modeling, spatial demography, and population modeling, as well as others outside of talk courses. I would seek mentorship opportunities both within in between department's given, the broad applicability of the themes and skill sets I bring to the University.

So hopefully this gives you an idea of what I can bring to the Department in the University and how my research themes align with the larger research themes of the University. I strongly believe that there is a lot of overlap in complementarity between myself, the individuals within in the ongoing work within the Department. I believe that the University of Liverpool I would be able to generate a robust and complimentary remote sensing program within the Department while continuing to carry out interdisciplinary and high impact research. Thanks for your time and your consideration.