It is surely a much observed fact that the community of folk involved in spatial and spatial related fields is a small but diverse group. People know each other across international boundaries. Its a shame therefore that research in this community is not internationally coordinated.
A privileged few (on review boards and committees) around the world are permitted regular insight into proposed and contemplated research projects internationally. This is a wonderful way to see just how the industry is shaping itself and in a small way, permits the steering of the industry to attempt the "really big problems".
However, with this insight comes a realization of two problems that seem to persist. The first of these is the admirably enthusiastic determination to solve a problem that has been solved already. As an example, there is almost always a project or two in a research portfolio aimed at conquering the problem of displaying points on a map. Firstly, this is not a problem that needs solving again. And secondly, we certainly do not need to build another map viewer to do this. There are plenty of brilliant, cheap or free map viewers available for a simple search. If suddenly a researcher comes up with a clever way of displaying half a billion points in a tenth of a second on a web viewer and making them usefully visible to the user, well then lets add that to an existing viewer and perhaps that research is worthwhile. Another favourite is map styling. Once the points are on the map then wouldn't it be great if they all looked like little robots with shadows behind them ? Maybe so but there is no need to build a style engine to allow the little robots to be drawn, scaled, coloured, haloed, shadowed and so forth. Its been done and done well - many times over. If however, a research project figures out how to emulate the wonderful artistic generalization we have seen in hand created maps then that's another matter.
The second problem is that very often, smart people in our community follow a similar path and arrive at a research project very similar to a project being contemplated elsewhere. It happens often that valuable research funds are expended in different places on very similar projects. Even more frustrating is that there are projects that stall at a problem area that another researcher has conquered or is on the verge of conquering. Without visibility into the research, the stalled project may stay that way.
There is one more factor in the spatial research area to consider. It appears that venture capitalists have figured out that location is a key investment point and they are interested in funding projects. There is plenty of evidence to support this. It is also true that there are research funds in many countries that are directed specifically at spatial problems. Unfortunately, many of these are country specific and so the funds are not always optimally deployed for the international success we need. International cooperation originating in a particular country has proven very difficult to foster.
A small international group has met several times now to consider these factors and has proposed a possible solution. Its called LINC and there is a web site that explains how this group might work.
The general idea is that there is an international group of experts whose responsibility is to keep track of essential directions of spatial research and help local organizations with coordination of projects to avoid repetition and duplication. Also, the group think that it should be possible to provide perspectives in directions that spatial research needs to continue to evolve.
Venture capitalists also seek guidance as to where the technology dollars should be invested for a future payoff and this group would be ideally situated to provide that guidance. It has been suggested that the group would stamp promising original research that pushes spatial forwards with a logo to encourage investment and visibility of the research.
Your thoughts on this endeavour would be much appreciated.
24 May 2016
04 May 2016
Spatial Data and the Internet of Things (and the Internet of Humans)
During the recent GeoLife Round Table in Gävle, Sweden, it occurred to me as I'm sure it has to many others before me that the use of spatial in the IoT is facing a fairly challenging roadblock.
The premise of the IoT is that some sensing device is attached to the Internet. If the device is physically accessible then the chances are extremely high that the location of the device is known and its readings are location enabled. If it is remote (perhaps a hostile environment or internal to a live entity for example) then the chances are still very high that the location of the device will be known. Finally, a device that is moving is very likely to be location tracked since the very fact that it is mobile will be relevant to the sensor readings. An example could be a personal 2.5PM particulate sensor (see this reference study). The personal sensors are capable of alerting wearers that it is time to vacate an area before damaging inhalation occurs. The readings from the sensor are however also part of a larger data set that can be used to reduce harmful particulates once the dangerous areas are seen and possible sources identified.
Sensor data can be assumed to be temporal in nature.
However, sensors will also have vastly varying periodicity. The above mentioned 2.5PM sensors may emit a reading every 5 minutes and this may well be sufficient granularity for monitoring particulates. A sensor on a vehicle will have to have a very much finer granularity since a change within a second can be critical. An astronomical observation on the other hand may be fine with readings every day.
Sensors will also experience dramatic amplitude variations. Again with the personal 2.5PM sensor, it is reasonable to expect that the wearer experiences very low readings until entering a hostile environment when the values will suddenly peak. There are also sensors that are smart enough to report only when they have something significant to report. For example a home flood sensor need not report until it senses water where there should be none.
Thus sensors have amplitude irregularities, periodicity variations and regularity differences.
Consider now that the same challenges are present in the location information that is recorded with the sensor reading. A pedestrian gets in a car and suddenly records substantial displacements. The wearer goes to sleep. The wearer goes inside a building and the location tracking is more granular than street addresses. The wearer boards a flight, turns off the sensor and turns it back on in another country.
Even so, it could be argued that for single sensors, the peculiarities of the spatio-temporal readings can be managed since they should be well understood. This however does not work at all when multiple disparate sensors are sources (and this of course is where the IoT and IoH really becomes exceptionally useful). This is where correlations are developed, where multiple inputs lead to dramatic insights and the power of the IoT is fully realized.
For the IoT and IoH to establish a data store that is readily accessible, usable without substantial pre-processing, the location and temporal data must be woven into a stream that is useful on both the time and location axes. And this stream has to accommodate but not distort the fundamental value of the sensor readings. Simply dropping the sensor readings onto a location continuum is just not going to work. There has to be understanding of the location significance of the readings and where they are. And the data cannot be averaged. The causal effects would be lost in the rounding process. And this cannot be done divested from the temporal axis.
Finally, in the spatial world it has become absolutely imperative that spatial data processing use the time axis as an integral element of the process.
The ideal of an ad hoc collection of sensors all contributing readings to a spatio-temporal domain to give a multi-dimensional view of a particular environment is truly exciting but the spatial professionals have an essential role to play in realizing this.
The premise of the IoT is that some sensing device is attached to the Internet. If the device is physically accessible then the chances are extremely high that the location of the device is known and its readings are location enabled. If it is remote (perhaps a hostile environment or internal to a live entity for example) then the chances are still very high that the location of the device will be known. Finally, a device that is moving is very likely to be location tracked since the very fact that it is mobile will be relevant to the sensor readings. An example could be a personal 2.5PM particulate sensor (see this reference study). The personal sensors are capable of alerting wearers that it is time to vacate an area before damaging inhalation occurs. The readings from the sensor are however also part of a larger data set that can be used to reduce harmful particulates once the dangerous areas are seen and possible sources identified.
Sensor data can be assumed to be temporal in nature.
However, sensors will also have vastly varying periodicity. The above mentioned 2.5PM sensors may emit a reading every 5 minutes and this may well be sufficient granularity for monitoring particulates. A sensor on a vehicle will have to have a very much finer granularity since a change within a second can be critical. An astronomical observation on the other hand may be fine with readings every day.
Sensors will also experience dramatic amplitude variations. Again with the personal 2.5PM sensor, it is reasonable to expect that the wearer experiences very low readings until entering a hostile environment when the values will suddenly peak. There are also sensors that are smart enough to report only when they have something significant to report. For example a home flood sensor need not report until it senses water where there should be none.
Thus sensors have amplitude irregularities, periodicity variations and regularity differences.
Consider now that the same challenges are present in the location information that is recorded with the sensor reading. A pedestrian gets in a car and suddenly records substantial displacements. The wearer goes to sleep. The wearer goes inside a building and the location tracking is more granular than street addresses. The wearer boards a flight, turns off the sensor and turns it back on in another country.
Even so, it could be argued that for single sensors, the peculiarities of the spatio-temporal readings can be managed since they should be well understood. This however does not work at all when multiple disparate sensors are sources (and this of course is where the IoT and IoH really becomes exceptionally useful). This is where correlations are developed, where multiple inputs lead to dramatic insights and the power of the IoT is fully realized.
For the IoT and IoH to establish a data store that is readily accessible, usable without substantial pre-processing, the location and temporal data must be woven into a stream that is useful on both the time and location axes. And this stream has to accommodate but not distort the fundamental value of the sensor readings. Simply dropping the sensor readings onto a location continuum is just not going to work. There has to be understanding of the location significance of the readings and where they are. And the data cannot be averaged. The causal effects would be lost in the rounding process. And this cannot be done divested from the temporal axis.
Finally, in the spatial world it has become absolutely imperative that spatial data processing use the time axis as an integral element of the process.
The ideal of an ad hoc collection of sensors all contributing readings to a spatio-temporal domain to give a multi-dimensional view of a particular environment is truly exciting but the spatial professionals have an essential role to play in realizing this.
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