Python for GIS Software: The integration of Python as a main scripting
language of ArcGIS (ESRI, 2020a) and QGIS (QGIS Development Team, 2020) allows
users to use the combined power of GIS software and Python for processing
spatial data. The ArcPy package ships with a desktop version of ArcGIS and
allows Python to access GIS tools with extensions, useful functions, classes,
and modules for processing geospatial data. This package helps users to write
Python scripts which can be run within ArcGIS or as standalone scripts. ArcGIS
API for Python (called Pythonic GIS API) gives access to a wide range of
modules, classes, and functions provided by ArcGIS Online and ArcGIS
Enterprise for web-based GIS solutions. Similarly, we can use Python with QGIS
in many ways, such as executing commands in the Python console within QGIS,
extending the functionality of QGIS by developing new plugins, creating
standalone Python scripts, and developing custom GIS applications using PyQGIS
API (PyQGIS, [n.d.]). Other popular GIS software like GRASS GIS (GRASS
Development Team, 2017) and SAGA GIS (Team, 2001) also support APIs for
Python.

APIs for Big Data Platforms: As it was mentioned in Section 1, spatial
libraries and packages of Python were developed for processing data in a
single-node environment. Therefore, we need to use parallel and distributed
computing platforms like Hadoop and Spark for processing larger sets of data.
There are a number of Hadoop APIs that allow Python users to access the Hadoop
MapReduce paradigm and distributed file system HDFS, which include Hadoop
Streaming, mrjob (Yelp) (Yelp and Contributors, 2018), Pydoop (Leo and
Zanetti, 2010), and Luigi (Spotify) (The Luigi Authors, 2020). One can write
and run MapReduce jobs on Hadoop using all these APIs, but the Hadoop
Streaming API ships with Hadoop as a native API. mrjob can also run MapReduce
jobs locally without Hadoop for testing purposes. Pydoop is tightly integrated
with Hadoop and provides full access to Hadoop APIs. Pydoop also supports
direct access to HDFS via its HDFS API. Moreover, PyArrow also includes an
HDFS client to access HDFS. On the other hand, PySpark (Development Team,
2016) is a native Spark API that enables Python users to interact with the
Spark programming paradigm for processing large datasets. Along with a rich
set of Python libraries, this API also allows Python users to use built-in
Spark libraries, such as MLlib (machine learning), Spark Streaming, and Spark
SQL and Dataframes. Besides, PySpark can process data stored in a distributed
storage like HDFS. However, these Python APIs add an extra overhead in
computation during big spatial data processing as they are developed as a
layer on top of Hadoop and Spark. Hence, DASK (Dask Development Team, 2016)
and RAPIDS (Team, 2018c) have emerged as parallel and distributed Python
libraries to mitigate this issue (see Section 6.4).

###  7.3. GIS Software

A GIS (Geographical Information System) is an integrated environment to
capture, store, analyze, and visualize all kinds of geographical data (raster,
vector, and network). GIS software is a useful tool for researchers,
scientists, or practitioners, who want to extract inherent knowledge,
patterns, and relationships from geographical data and analyze the data to
address real-world problems. There are a few commercial and open-source GIS
software applications available, such as ArcGIS (ESRI, 2020a), QGIS (QGIS
Development Team, 2020), GRASS (GRASS Development Team, 2017), and SAGA (Team,
2001). ArcGIS and QGIS are the most popular GIS software among them.

ArcGIS (ESRI, 2020a) is a leading commercial GIS software application
developed by ESRI (ESRI, 2020c). Whereas, QGIS (Quantum GIS) (QGIS Development
Team, 2020) is a popular open-source GIS software application that supports
similar functionalities to ArcGIS. The desktop version of ArcGIS only supports
the Windows platform, but QGIS is available for all popular computing
platforms like Windows, Mac, and Linux. As QGIS has integrated a few other
popular GIS software like GRASS (GRASS Development Team, 2017), SAGA (Team,
2001), and OTB (Orfeo Toolbox) (Grizonnet et al., 2017), one can use a subset
of algorithms from these third party GIS software within QGIS. However, if we
want to use full functionality of these GIS software systems, we need to stick
with GRASS and SAGA (Lovelace et al., 2019). Both ArcGIS and QGIS have support
for a Python console that allows users to execute the functionality of GIS
tools and Python within ArcGIS and QGIS. Also, both of them have an interface
to Python and R. Therefore, one can use the functionality of ArcGIS and QGIS
within Python and R programming environments. According to an experiment
conducted by Debicka et al. (Debicka and Szombara, 2018) based on the buffer,
convex hull, and intersection geometric operations, QGIS is faster than ArcGIS
for processing spatial data. However, in terms of spatial mapping
capabilities, ArcGIS is way better than QGIS. ArcGIS is also richer in terms
of tools and supported algorithms, but one can extend the capabilities of QGIS
by adding third-party plugins. Moreover, the supported input file format of
QGIS is very rich, as it uses the GDAL/OGR library.

Like Spatial RDBMSs, GIS software is also going through many changes over the
years to adapt to this era of big spatial data. ESRI has released open-source
GIS tools for Hadoop (ESRI, 2013) to perform analysis on big spatial data by
utilizing the distributed processing capability of Hadoop. These tools
include: (i) a geometry API for Java users to develop MapReduce applications
for spatial data analysis; (ii) a spatial framework, which allows users to
perform SQL-like queries on spatial data using the Hive Query Language (HQL);
(iii) a geoprocessing toolbox, which allows users to take advantage of both
Hadoop and ArcGIS for spatial processing; and (iv) a complete toolkit
containing a geometry API, a spatial framework, and a geoprocessing toolbox.
ESRI has also introduced a Spark-powered GeoAnalytics toolbox for both ArcGIS
server and desktop versions. GeoAnalytics Desktop (ESRI, 2020b) brings
parallel processing of data across multiple cores of a personal computer
through ArcGIS Pro. Whereas, GeoAnalytics Server (ESRI, 2019) provides
distributed processing of big spatial data across multiple nodes of a cluster
running ArcGIS Enterprise. As Spark ships with ArcGIS, users do not need to
install Spark separately. Users can also write a Python program to process
data using the GeoAnalytics toolbox. Besides, ESRI ArcSDE is an RDBMSs gateway
that allows ArcGIS users to store, use, and manage spatial data in some
popular databases, such as IBM DB2 and Informix, Oracle, Microsoft SQL Server,
and PostgreSQL. In summary, GIS software is continuously adapting to process
big spatial data.

###  7.4. Future Research Directions

At present, Python and R support a rich set of libraries and packages for
processing spatio-temporal data. These libraries and packages are developed
for processing data in a single node computer system and are not suitable for
processing big data. Python and R users could utilize the available APIs for
big data processing platforms, such as Hadoop and Spark. But we do not know
how these libraries will perform with big data platforms since there is no
comprehensive evaluation yet. Besides, these APIs will add an extra overhead
in overall the computation since we need to compile Python or R code into the
target platform. We can also implement APIs to use big spatio-temporal systems
that have been developed based on big data platforms. Currently, DASK and
RADPIS are promising platforms to process big spatial data for Python users. R
users either need to use an existing big spatio-temporal data processing
system or need to develop a system like DASK.

On the other hand, GIS software will be adding new features and modules to
create analysis and mapping facilities for a wide range of new application
domains. As machine learning (ML) and deep learning (DL) algorithms and
techniques are important for solving complex spatial problems, there will be
more ML and DL models in GIS software in the future. Therefore, along with
parallel and distributed computing, more research is required in terms of
integrating ML and DL tools with GIS Software.

##  8\. Conclusion

Due to the rise of spatio-temporal data volume and the significance of
extracted knowledge in a wide range of application domains, plenty of research
and development works have been done in the area of spatio-temporal data
analytics in the past decade. Survey work is always pivotal for researchers to
know and advance the state of the art. In this survey, we have conducted a
comprehensive study on the whole ecosystem of spatio-temporal data analytics,
which covers spatial databases (SQL and NoSQL), big spatial-temporal
infrastructures, programming languages, and software tools. This study also
addressed the importance, current demand, and future of spatio-temporal data
analytics. Though the main focus was on spatio-temporal analysis for big data,
we have discussed related areas as well.

We argue that the research community needs to address a few areas of spatio-
temporal data analytics in future research that include (i) integrating more
support to model and analysis of spatial raster data, (ii) integrating more
support for processing spatio-temporal (trajectory) data streams, (iii)
integrating or improving SQL-like queries, and (iv) adding more support for
analysis and visualization in big spatio-temporal infrastructures. It is
already evident that there will be more research on integrating AI, machine
learning, and deep learning models in future big spatio-temporal
infrastructures for uncovering hidden knowledge. There will be demand for
integrating visualization support for big spatial data in web platforms.
Besides, we think future infrastructures will be more application-specific,
such as IoT, neuroscience, emergency management, transportation, and other
applications. The usage of GPU in RAPIDS has shown significant speed-up in
computation compared to other infrastructures, and therefore, more research is
required in terms of using GPUs for spatio-temporal data analytics. We hope
that the accumulated information in this study will be useful for researchers,
practitioners, and developers who are currently working or who want to work in
the area of spatio-temporal data analytics.

###### Acknowledgements.

The work of L. Torgo was undertaken, in part, thanks to funding from the
Canada Research Chairs program and NSERC.

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# Graphene amplifier reaches the quantum-noise limit

Kin Chung Fong<EMAIL_ADDRESS>Raytheon BBN Technologies, Quantum
Engineering and Computing Group, Cambridge, Massachusetts 02138, USA

To make yourself heard in a noisy environment is no easy task. An amplifier,
like a megaphone, can come to your rescue by increasing your voice’s volume
over the background noise. Your speech can be heard clearly. This is analogous
to measuring superconducting qubits. Since their energy quanta are a few
orders of magnitude smaller than the thermal noise, amplifiers are necessary
to boost the signal up before being registered by apparatus at room
temperature. However, having a high gain from amplifiers is not nearly enough.
The physical process of amplification is also subjected to fluctuations,
resulting in added noise by the amplifier that can degrade the signal-to-noise
ratio. For a phase-insensitive linear amplifier, the minimum amount of this
added noise is half a quantum because of quantum fluctuationsCaves (1982).
Employing a parametric process to achieve this fundamental limit of
amplification at radio and microwave frequencies has a long history: from
using variable-capacitor diodes in the 1960s to Josephson junctions in the
1980sYurke et al. (1987). With high gain and low noise, modern Josephson
parametric amplifiers (JPAs)Castellanos-Beltran and Lehnert (2007) have
quickly become a must-have in laboratoriesAumentado (2020): enabling high-
fidelity qubit readouts, observing quantum jumps, tracking quantum trajectory,
and even searching for the rare event when the axion dark matter converts into
a microwave photon under a high magnetic field.

Writing in Nature Nanotechnology, two independent reports by Guilliam
Butseraen, et. al.Butseraen et al. (2022) and Joydip Sarkar, et. al.Sarkar et
al. (2022), now further advance JPAs using a two-dimensional
material—graphene.

Figure 1: (a) Parametric amplification in a mechanical oscillator. The red dot
marks the center of mass of the child in the swing. Its oscillation
periodically modulates the effective length of the pendulum, and thus, the
resonant frequency. (b) Schematic model of the parametric amplifier at radio
or microwave frequencies. The signal will enter the LC resonator and be
amplified by the modulation of the inductance before leaving the resonator.
(c) A SQUID as an implementation of the variable inductor. The Josephson
inductance depends inversely on the total critical current, which is
controlled by the magnetic flux through the loop. (d) Josephson junctions
based on superconductor-insulator-superconductor (SIS) heterostructure. (e)
Graphene-based Josephson junction with a gate control can operate as a
variable inductor. (f) Josephson junctions based on superconductor-normal
metal-superconductor (SNS) lateral junctions. The latest reports demonstrate
JPA by SNS junction with graphene, encapsulated in hexagonal-boron nitride
(blue), as the weak link. This new JPA is voltage-tunable and can operate
under a high-magnetic field.

We can consult children on swings about the process of parametric
amplificationRugar and Gr tter (1991) (Fig. 1a). A child can amplify the
pendulum oscillation by standing up and squatting down when the swing reaches
its maximum and minimum height, respectively. This stand-and-squat action is
pumping the pendulum motion at twice its resonant frequency, and doing work on
the harmonic oscillator. The amplitude of the originally small oscillation
(signal) will gradually increase, i.e. amplify. Parametric amplification sets
apart from other amplification mechanisms because it modulates the reactance,
rather than the resistance of the system. As such, it minimizes the noise that
is inevitably brought into the amplification process according to the
fluctuation-dissipation theorem.

A JPA constructed by a superconducting LC resonator, schematically shown in
Fig. 1b, operates under the similar principle. In addition to suppressing
dissipation, superconductors can form Josephson junctions, which can provide
an inductance—from the inertia of the Cooper pairs tunneling through the
junction—to the circuit. When two Josephson junctions are made in the form of
a loop, their supercurrents will interfere and form a device known as
superconducting quantum interference device (SQUID) (Fig. 1c-d). Due to the
Aharonov-Bohm phase, the total critical current of the SQUID depends on the
magnetic flux through the loop. A magnetic field generated by an electrical
current can control the Josephson inductance. Hence, we can pump the JPA by
modulating its resonant frequency with a pump current at twice the frequency
of the superconducting resonators. Alternatively, JPAs can be powered up by
feeding the pump tone directly into the input port for parametric
amplification. The second method exploits the non-linear dependence of
Josephson inductance on the magnitude of the current running through the
junction, which allow for non-degenerate parametric amplifications. JPAs
operate as a reflection amplifier: when the minute signal enters the JPA via
the coupling capacitor, it is parametrically amplified before making its way
back to the input port.

The two research teams now replace the insulator traditionally used as the
Josephson weak link with a layer of graphene encapsulated by hexagonal-boron
nitride (Fig. 1e-f). By doing so, the researchers can employ the gate voltage
response of the graphene to tune the Josephson critical current and thus the
Josephson inductance. Hence, they can control the resonant frequency of the
JPA by voltage, rather than current. Butseraen, et. al. attain parametric
amplification using a transmission-line resonator at $\sim$5 GHz and a pump
tone through the gate; whereas Sarkar, et. al. exploit lump components and a
pump tone applied at the input port, respectively. They both achieve similar
figures of merit: $\sim$10 MHz bandwidth, $\sim$500 MHz tuning of the resonant
frequency with a gain of $>$20 dB, and 1-dB compression point at about -127
dBm of input power. Most importantly, both teams show that the added noise
from the graphene JPA can reach the quantum limit. This is a pleasant surprise
given that graphene Josephson junctions are more dissipative with a lower
quality-factor at microwave frequencyHaller et al. (2022), than the
conventional superconductor-insulator-superconductor (SIS) junctions. With
their careful designs and implementations, the two teams demonstrate that a
superconductor-normal metal-superconductor (SNS) junction can overcome its
intrinsic dissipation to achieve quantum-limited amplification.

Given the ubiquity of JPAs based on SQUIDs, it is natural to question the
utility of graphene JPA beyond mere scientific curiosity. One of the potential
benefits may lie in the mitigation of cross-talks among different tunable
components in the quantum circuitry due to the magnetic field generated by a
current bias. Without the SQUID loop, graphene JPAs can also be more densely
packed and operate under a larger in-plane magnetic field. Moreover, these
latest results exemplify the renewed interest in applying SNS junctions to
quantum science. Unlike their insulating counterpart, SNS junctions possess
more variety in their physical properties, arising from the interplay of the
superconductor and the materials in the weak link. Not only would it enable
voltage control of quantum amplifiers as shown here, but, by developing this
motif, we can also create new hybrid superconducting electronicsBaselmans et
al. (1999), qubitsWang et al. (2019); Hays et al. (2021), high-sensitivity
photon detectorsWalsh et al. (2021), and topological superconductivity which
may host Majorana zero modesFornieri et al. (2019). With the rise of quantum
technology, we shall expect more innovations from SNS junctions to come!

The author declares no conflict of interest.

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