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         Geoplanning 
Vol 5, No. 2, 2018, 205-214                                                                                                                                                         Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

                doi: 10.14710/geoplanning.5.2.205-214 

POTENTIAL OF BIG DATA FOR PRO-ACTIVE PARTICIPATORY LAND USE PLANNING 

W.T. de Vriesa 

a Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Germany 

 

Abstract: The presence of (spatial) big data presumes that citizens can more actively 

collect and analyse data for their own land use goals. This article evaluates that claim. 
Given that land use planning heavily depends on participation and citizens own 
contributions the core question is whether and how (spatial) big data can enhance and 
or complement current land use planning endeavours. The article starts by defining and 
conceptualising the various phases and objectives of land use planning. This is needed 
to verify where citizen participation can play a crucial role and where bottom-up 
influence can aactually emerge. The article is fundamentally explorative. It relies on 
evaluating existing websites and documentation which conceptualise (spatial) big data 
and smart application, with a particular emphasis on ‘smart people’. A number of 
specific cases are explored in order to verify how and in which type of land use planning 
activity citizens are actively. The evaluation indicates that many the smart application 
making use of big data are still largely driven by conventional hierarchical governance 
structures. The choice of data and associated analytics are still largely confined and 
opportunities whereby the designs of the new and alternative land use option by citizens 
are accepted or adopted is still limited. The take-home message is that adoption of big 
data for the purpose of empowering citizens is still limited. There probably needs to be 
more exemplary projects and various forms of capacity development and exploratory 
pilots before the full potential of (spatial) big data can be employed for bottom-up land 
use planning. 

 

 Copyright © 2018 GJGP-UNDIP  

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

de Vries, W.T. (2018). Potential of Big Data for Pro-Active Participatory Land Use Planning. Geoplanning: Journal of Geomatics and Planning, 5(2), 205-

214. doi: 10.14710/geoplanning.5.2.205-214 

1. INTRODUCTION 

Globally the impacts of land use, land occupation and allocations of land rights are changing. Emerging 
effects include increasing land scarcity (Gerber, Hartmann, & Hengstermann, 2018), rapid urbanization and 
growing hazards whereby ever larger numbers of people are at risk. This situation calls on the one hand for 
information sources which are available instantly and which have better quality, reliability and actuality, and 
on the other hand for planning processes which rely on more active participation and co-creation of citizens 
and enhanced informed decision making mechanisms.  

The rise of big, open, linked and voluntary data is claimed to (partly) address the former, whereas the 
renewed interests in concept of the ‘right to the city’ (Brenner et al., 2012; Mayer, 2012), the experiments of 
co-creation of spatial design and spatial governance (Franz, Tausz, & Thiel, 2015; Rooij & Frank, 2016), and 
the occurrence of neo-cadastres (De Vries, Bennett, & Zevenbergen, 2015) amongst others may address the 
latter. What are these developments however, and to which extent are they truly changing the landscape 
and the practice of spatial planning? The main research question of this article is Does the presence of spatial 
big data-(1) increase the number of citizen-driven land use planning contributions; (2) Improve the quality 
with which citizens can actively collect and analyse data to pursue their own land use goals; and (3) make 
citizens smarter. 

OPEN ACCESS 

Article Info: 
Received: 30 June 2018 
in revised form: 15 Sept 2018 
Accepted: 15 October 2018 
Available Online: 25 October 
2018 
 

Keywords:  
Big data, Land use planning, 
smart cities, land management, 
participation. 

 
Corresponding Author: 
Walter Timo de Vries 

Technical University of Munich, 

Germany 

Email: wt.de-vries@tum.de  

  

https://orcid.org/0000-0002-1942-4714
https://doi.org/10.14710/geoplanning.5.2.205-214
https://doi.org/10.14710/geoplanning.5.2.205-214
mailto:wt.de-vries@tum.de


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This article discusses first qualifications and appraisals of big data, with a particular focus on geospatial or 
geotagged/georeferenced big data. This discussion also highlights a number of current concerns, i.e. dangers 
for privacy, unequal access, digital divides, etc. After this review on spatial big data, it discusses the variations 
in spatial and land use planning. This discussion is necessary to understand where and how big data can play 
a role in which parts or which phases land use planning. This includes a review of goals, tools and instruments 
in the different types of spatial planning, including the role of geospatial tools and instruments in spatial (land 
use) planning.  

Part of this discourse is a specific focus on smart cities and smart (rural) regions. It is therefore crucial to 
understand how big data are influencing the ‘smart’ land use planning. This article will focus specifically on 
the element of ‘smart people’ and a classification of how and where smart people play a role in different 
actions and phases of land use planning. With this classification different examples in Germany of smart 
people applications in spatial planning are discussed and reviewed in order to answer the research questions 
more specifically. The conclusion section then discusses how the research questions can be answered and 
what sort of further research is required to obtain a better understanding of the role and potential of big data 
in spatial land use planning.  

 

2. DATA AND METHODS 

2.1. Qualification and Appraisals of (Spatial) Big Data 

Big data have gained a significant place in the discourse of multiple domains. However, in these discourses 
one can also observe a number of developments and variations in understanding and defining big data. Batty 
(2013) characterizes big data as ‘any data that cannot fit into an Excel spreadsheet’. However, size is not the 
only characteristic of big data.  Schintler & Chen (2017) and Doornik & Hendry (2014) classify further that big 
data can be ’fat’ or ‘tall’. Fat data has many attributes ‘M’ but lesser number of observations ‘N’ , while tall 
data has few attributes but many observations (M<N).    

French, Barchers, & Zhang (2015) compare the structure of big data to traditional data indicating big data 
infrastructures are far more ‘unstructured’: many of these records are tagged with geolocation or a time 
stamp, and sometimes both, time or location can often be used to join otherwise unrelated data sets. In 
addition to this traditional structured data, we now have vast amounts of unstructured data (e.g. drone 
videos, Tweets, Facebook posts, YouTube videos, foursquare check-ins, surveillance videos and much more). 

The EUCLID Project (http://euclid-project.eu/modules/chapter6.html) refer to big data as having  three 
key aspects, referred to as the 3Vs of Big Data: variety, volume and velocity (Laney, 2001; Schintler & Fischer, 
2018) add a fourth dimension: Veracity. This has a lot to do with trueness and uncertainty of the sources and 
validity of the data. Discussions on fake news Facebook and those of Cambridge Analytica have also given rise 
to look critically at who owns which data and how third parties use data. Regardless of these, Table 1 
summarizes the characteristics of these 4Vs. Recent discourses even add a 5thV, namely Value. However, this 
specific V does not yet occur in many other literature sources, despite its obvious relevance. Another 
distinction related to big data is the difference between ‘big’ and ‘small’ data. Kitchin & Lauriault (2015) list 
the following differences (Table 2). 

Table 1. Thee 4Vs of big data 

 Variety Volume Velocity Veracity 

Data characteristic Structured, semi-
structured and 
unstructured 

Large volumes of data Streams, sensors, near 
real-time data, IoT 

data may come from 
unknown or 
everchanging sources 

Challenge Data integration Reasoning and 
querying 

Reasoning & querying quality of data is 
uncertain and from 
unverified sources 

Solution Semantic technologies 
are a good fit 

Distributed storage & 
processing, parallel 
processing 

Stream reasoning & 
querying 

avoiding noise and 
abnormalities in the 
data 

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Table 2. Differences between big and small data  
 

Small data Big data 

Volume Limited to large Very large 

Exhaustivity Samples Entire populations 

Resolution and identification  Course & weak to tight & strong Tight & strong 

Relationality Weak to strong Strong 

Velocity Slow, freeze-framed/bundled Fast, continuous 

Variety Limited to wide Wide 

Flexible and scalable Low to middling High 

 
The manner in which big data can be constructed or sourced depends largely on the manner in which these 
data are generated. On the one hand, one can distinct active to passive sensors or technical and human 
sensors, on the other one can make a distinction between user-generated, transaction-generated and sensor-
generated big data. The crucial differences in the latter qualification are given in Table 3.  
 

Table 3. Data sources of big data  (Senatsverwaltung für Stadtentwicklung un Wohnen Berlin, 2017) 

User generated data Transaction-generated data Sensor-generated data 

Photo-platforms Mobility platforms Navigation systems 

Rating-platforms Real estate portals Mobile phone data 

Map portals Hotels platforms Surveillance data 

Social media Tenders Building data 

GPS tracking Exchange / sharing platforms Biometric data 

Search portals Job platforms 
 

Wikis Online business 
 

Dating portals Mobility data 
 

Business networks 
  

 
Constructing big data can be done in multiple ways. The comprehensive document by the 

Senatsverwaltung für Stadtentwicklung un Wohnen Berlin (2017) lists these possibilities: (1) Via Application 
programming interfaces (APIs) – e.g. Twitter, Flickr, Openstreetmap; (2) Via Webscraping – extracting of data 
via websites – clicking on links, completion of forms, scrolling; (3) Via making data sources available for 
commercial purposes; (4) Data brokers – e.g. Airdna (for Airbnb), Gnip (Twitter and diverse data sources of 
user-generated data ) and Quintly; (5) Via communities – e.g. data journalists, open communities.  

This list shows indeed the large variety in platforms, content, shapes and formats. What makes big data 
‘spatial big data’ is the specific georeference. Schintler & Chen (2017) indicate how such georeference can be 
added to the data being constructed through the above listed possibilities and thus create spatial big data: 
(1) Geo-tagged photos; (2) Weather data (hourly, daily); (3) GPS trajectories; (4) SNS Check-in records (twitter, 
facebook, etc.); (5) Earth observation imagery; (6) Public transportation card transactions; (7) Spatial events, 
e.g., crimes, accidents; and (8) Climate model simulations. 

Research based on spatial big data requires however spatial analytics. The analytics needs to capture for 
example certain spatial patterns which cannot be seen by simple visual observation, and needs to be able to 
make predictions based on such patterns. Spatial analytics, in other words, the capability to automatically 
derive predictions of spatially distributed features and phenomena, patterns of spatial collocation of factors 
or indicators which were previously not considered connected, finding hotspots of certain manifestations of 
phenomena, and needs to find changes and outliers in spatial patterns.  

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2.2. Changes and Variations in Land Use Planning 

It is however not only the manner in which data are being constructed which is changing, also the way of 
land use planning is conducted changes. Although literature is fairly consistent on what land use planning 
entails and what its goals are, at the same time there are variations on a similar theme. These variations have 
to do with different emphases in the professional and/or institutional set-up of land use planning systems in 
different countries and in the degree and type of experience gained with land use planning in different 
environments.  

A commonly used classification of land use planning is that from GIZ (Wehrmann, 2012), which refer to 
land use planning as an iterative process of six phases (Figure 1): definition of objective and approach; 
analysis; plan formulation; approval; implementation; monitoring. Each phase has distinct characteristics and 
requires specific kinds of (spatial) data and associated (spatial) and social analytical tools. Lagopoulos (2018) 
uses a different kind of description, referring to eight interconnected stages or actions (Figure 2): decision to 
intervene; survey of spatial system; policy making (alternative scenarios); forecasting; model of spatial 
system; alternative spatial scenarios; evaluation and selection; implementation. Metternicht (2018) list 
different variants of land use planning, which either reflect different traditions or different foci. Table 4 list a 
number of such variants.  

 

 
Figure 1. GIZ phases of land use planning (Wehrmann, 2012) 

 
 

 
 

Figure 2. Land use planning stages or actions (Lagopoulos, 2018) 

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Table 4. Examples of land use planning variants (Metternicht, 2018) 

Variants of land use planning Description 

Land use planning 
 

Systematic assessment of land and water potential, 
alternatives for land use and economic conditions, in 
order to select and adopt the best land use options 
 

Spatial land use planning 
 

Interdisciplinary and comprehensive approach directed 
towards balanced regional development, and the 
physical organization of space according to an overall 
strategy 
 

Integrated land use planning 
 

Assessment and assignment of use of resources, taking 
into account different users, including all agricultural 
sectors- pastoral, crop and forests – as well as industry 
and interested parties 
 

Participatory land use planning 
 

Planning of communal or common property land, 
important in many communities where lands are 
degraded, and where conflicts over land use rights exist 

Regional land use planning 
 

Process of territorial development designed to facilitate 
the elaboration of a general spatial concept and land use 
priorities 

 

2.3. Smart cities and planning 

Once the spatial data are connected and/or integrated to the various forms, phases and spheres of land 
use planning, one can start to speak about smart land use planning. Being or acting smart assumes however 
a number of things. Literature on smart cities makes a specific set of characteristics whereby ‘smartness’ can 
be evaluated: smart economy, smart mobility, smart governance, smart environment, smart living and smart 
people. The benchmarking ranking model by the smart cities projects (http://www.smart-cities.eu) employs 
these six characteristics to rank cities in Europe on the degree of smartness. Crucial for land use planning is 
hereby the connection between technologies which may be employed for each of the phases on land use 
planning but also the degree to which people can actively contribute by creating their own data.  

The latter is evaluated in the ranking of smart people, which is ranked specifically as well (see Figure 3). 
What is remarkable in this project with respect to this specific article is that the German cities relatively rank 
low on this list regarding the smart people characteristic. This is not to say that German cities are not smart, 
but that the degree, to which people are actively contributing to the smartness of the cities, including the 
usage of big data, seems relatively low.  
 

 

Figure 3. Excerpt from the European smart cities project (http://www.smart-cities.eu) 

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3. RESULTS AND DISCUSSION 

3.1. German cases of use of big (spatial) data and smart planning 

In order to evaluate the usage of spatial big data more specifically in Germany cases, examples were 
extracted from the following sources: (a) Big Data und Crowd Data für die Berliner Stadtentwicklungsplanung 
1; (b) European list smart cities TU Wien 2; (c) Smart Cities in Deutschland: So digital Sind unsere Städte 
wirklich3; (d) Digitale Stadt4; and (e) Land atlas Germany5. This list is by far complete, but it provides a good 
first insight in where and how data can be used specifically for land use planning. The study ‘Big Data und 
Crowd Data für die Berliner Stadtentwicklungsplanung’ (2017) qualifies the usage of spatial big data by ‘smart 
people’ in land use planning based on two dimensions, there are the degree to which the targeted land use 
interventions are either specific or general, and the degree to which the targeted interventions are initiated 
and generated from the citizens (bottom-up) or by the government (top-down). With these two dimensions 
the examples can be qualified as in Figure 4. Table 5 lists examples, which were evaluated fitting these 
classifications of Figure 4. 
 

 
 

 
Figure 4. Qualification of smart people in land use planning 

 
 

                                                             
1 https://www.stadtentwicklung.berlin.de/planen/basisdaten_stadtentwicklung/big-data/downloads/big-data_crowd-
data_berlin.pdf. 
2 http://www.smart-cities.eu/ 
3 https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility 
4 https://www.bbsr.bund.de/BBSR/DE/Stadtentwicklung/StadtentwicklungDeutschland/digitale-stadt/digitale-stadt-
node.html 
5 https://www.landatlas.de 

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https://www.stadtentwicklung.berlin.de/planen/basisdaten_stadtentwicklung/big-data/downloads/big-data_crowd-data_berlin.pdf
http://www.smart-cities.eu/
https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility
https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility
https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility
https://
https://
https://www.bbsr.bund.de/BBSR/DE/Stadtentwicklung/StadtentwicklungDeutschland/digitale-stadt/digitale-stadt-node.html
https://www.landatlas.de/


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Table 5. Examples of land use planning with spatial big data 

 Specific   General 

Top down Ludwigshafen 
diskutiert 

Wasserstadt dialog 
hannover 

dialog luft und lärm 
leipzig 

radständer nürnberg 

München Mitdenken 

Potsdam weiterdenken 

Place2help Rhein-Main 

Bürgerhaushalt Berlin-
Lichtenberg 

Sag's doch 
Friedrichshafen 

Flashpoll 

Die urbanauten 
münchen 

Leerstandsmelder 

Critical mass Dresden 

Bauleitplanung Lingen 

Planportal Hamburg 

Digitale Dörfer 

Nebenan 

Bottom-up Recht auf Stadt 
Hamburg 

Viva Viktoria Bonn 

Nordstadtblogger 
Dortmund 

Urbanophil berlin 

openberlin 

Frankfurt gestalten 

Hackaton Freiburg 

Code for Berlin 

 
A number of these examples are discussed in more detail. Recht auf Stadt Hamburg (‘Right to the city 

Hamburg), this specific case is part of a larger network http://www.rechtaufstadt.net/ . This website is a 
specific case of a bottom-up protest facility, providing an internet platform for citizens to share information 
on land use and property issues which are considered unfair, and on upcoming actions and events which 
require the mobilisation of people (such as protests, online petitions or demonstrations). The online facility 
currently focuses primarily on the high rents in cities and on the increasing difficulty for ordinary people to 
live affordably in the city. Currently there are however not so many maps or spatial big data used, although 
there would certainly be a potential for this. Examples such as https://www.antievictionmap.com/ in 
California/USA, showing maps, which display the location and degree of gentrification and locations of 
evictions for example, show that it is possible to go one-step further than simply informing and signalling 
problems. Instead one can advocate and using blame and shame techniques with such maps. Currently, 
however the Recht auf Stadt facility remains in the problem-framing sphere, instead of scaling it up to full 
collaborative problem solution tool.   

The Leerstandsmelder (‘Reporting vacant land and vacant buildings’) is an example of an awareness 
raising facility developed by both local governments and citizens. The site uses a map facility to collect and 
display information about the location and type of vacant / fallow land and/or buildings. As such it can provide 
both governments, private parties and citizens an idea of where unused or underutilized land exists, and 
where potentially active land use planning measures could be taken in order to revitalize the land or building. 
As such it offers a good insight in potential development problem areas and it could help to mobilize resources 
to act upon this information. It actively makes use of webGIS technology to manage, locate and display the 
information. What is missing in this facility is, however, the possibility to interact with the information. 
Citizens are not able to actively engage with the facility with suggestions or requests, or to link the 
information to other types of (spatial big) data to see possible reasons or trends. Theoretically, such spatial 
analytics would be feasible. Mapreduce models and Hadoop open source software 
(https://hadoop.apache.org/) could potentially play a significant role here. Collaboration and engagement 
are enhanced with this tool in land use planning, but scenario building and seeking bottom-up based land use 
planning solutions is not yet possible.       

Code for Berlin - https://codefor.de/berlin/  is an example of an open data project. Originating from a 
series of Hackatons, whereby voluntary programmers design code to generate software solutions for a given 
problem, it now functions as a platform whereby all types of algorithms and software can be shared. The 
openness makes it accessible for any citizen, so engagement is unlimited in theory. However, programming 
remains a complicated activity for many ordinary citizens and therefore remains a rather exclusive activity. 
Hence, the theory does not always translate practice. The scenarios and solutions may thus be limited to 
those are capable of handling the technology and as a result the technology is not value-free. At the same 

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time, there are also a number of intermediaries which can play an active role in making the technology more 
accessible. Initiatives such as 52 North (https://52north.org/) and Runder Tisch GIS 
(https://rundertischgis.de/) aim at enhancing the utilization of open source spatial technologies in the field 
of spatial land use planning (Figure 5). Yet, as said, there is still a certain professional threshold to make this 
a fully citizen-based technology.    

The project LandAtlas (https://www.landatlas.de) is a very independent project, but worthwhile 
mentioning here as it focuses specifically on rural areas. Combining different publicly available datasets (such 
as the ones published by the national planning and statistical agencies) it aims at deriving a rich picture of the 
current situation in rural areas. It uses thereby an integrated indicator on ‘rurality’, comprising of 5 other 
integrated indicators: Built-up area density, Proportion of agricultural and forest land, Proportion of family 
houses, Regional population potential and Accessibility to major centres. The combination of all these 
indicators, which are all georeferenced or geotagged, leads to a product LandAtlas. Through the LandAtlas it 
is possible to research interactively possible spatial correlations or associations at the lowest administrative 
scale. This type of information may be highly useful for citizens who aim to fact-check regional development 
indicators (e.g. is our region really shrinking or are only certain municipalities affected) and for those who 
may signal certain consistencies and inconsistencies (e.g. are public finances in line with gross local products 
or degree of public and private debts).    

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. Big data for land Management in Germany (https://rundertischgis.de/) 

 

3.2. Discussion 

From the examples presented above one can observe that indeed many examples exist in Germany 
whereby citizens are actively or passively involved with spatial big data in different phases of land use 
planning. With reference to the typologies of Lagopoulos (2018), Metternicht (2018) and Wehrmann (2012) 
one could observe that most of the applications appear in both the definition and the plan formulation phase 
(using Wehrmann (2012)), the survey of social system and the policy making stage (using Lagopoulos(2018)) 
and the traditional land use planning variant (using Metternicht (2018)). This clearly leaves a number of 
potential incorporations, especially in the phases and stages of developing and comparing alternative 
scenarios, and in those critically evaluating the effectiveness of certain land use planning decisions, 
untouched.  

The examples reveal also that maps and other forms of spatial data are not yet crucial in many strategic 
decision processes, and if they are, they heavily rely on professional geospatial technology expertise. The 
‘non-expert’ examples are currently employed to signal, describe, display and categorize problems and to 

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find and highlight issues. In addition there are examples whereby citizens advocate alternative forms of action 
and mobilise people, yet these are without any maps or spatial analytical functionalities. The examples given 
in Table 5 clearly indicate these types of actions. On the other hand, there are just very few good examples 
found in Germany whereby citizens - with the use of big spatial data - actively indicate or prepare land use 
zones, analyse the effectiveness or efficiency of public restrictions and sanctions, intervene in land use 
acquisition processes, demand alternative locations or blame and shame decisions, which were against public 
agreements. This seems to indicate that there are still spheres of land use planning where the usage of big 
spatial data is still limited. When we compare these spheres one could distinct four spheres in which citizens 
could potentially contribute in land policy formulation, land politics and organization of land and active 
contributions to land use planning decisions (Table 6). 

Table 6. Four spheres of land use planning 

 
Focus 

Land Policy 
Formulation 

Land Politics (Public) Organization 
of Land 

Citizens as 
contributors to land 

use planning 

 
Traditional 

Land use planning 

Policy process =  
Problem framing 
and agenda setting,  
policy development 
and decision making,  
policy 
implementation, 
policy control 

Representation, 
idea generation and 
consideration, 
decision making and 
deliberation in 
parliament, 
democratic 
supervision 

Executives; 
Strategic top; 
Operating Core; 
Technostructure; 
Support Staff 
  

Citizens as rulers 
(participants in land 
use policy processes) 
Citizens as ruled 
(subject to authority, 
recipients of land 
use plan) 

Smart big data 
driven Land use 

planning 
 

Policy 
implementation; 
framing; protest 
  

Democratic 
supervision; 
signaling problems 
(awareness & 
information) 

Operating core; 
open portals; 
presenting 
integrated 
information  

Citizens as 
consumers of 
services 

 

4. CONCLUSION 

In the context of the growing availability of spatial big data and in the light of the assumption that citizens 
use these spatial big data effectively and responsibly, this article questions the extent to which this 
assumption is valid.  Based on an exploratory analysis of usage of big (spatial) data in Germany in the field of 
spatial planning the provisional conclusions are that there is indeed evidence of citizen-driven land use 
planning contributions, and the number of sites and the combined use of big data increases. Furthermore, 
one could see that the big data such as the ones assembled by LandAtlas start to derive new insights in local 
land use facts. Such new insights might support new discourses in what types of actions are useful and 
relevant. At the same time, the quality with which citizens can actively collect and analyse data to pursue 
their own land use goals can still be improved. There are indeed some active contributions, but there are also 
still many ‘fake’ or ‘opportunistic’ contributions. Hence, regarding the big data characteristic veracity there 
are still problems.  

Regarding the overall question - do big spatial data already make citizens smart(er)? – One can only state 
partly. Despite the many cases of open issue developments and voluntary code development often citizens 
are still more consumers of the smart applications and not so often producers of complete and verifiable 
data. Smartness, in other words, therefore still seems to be more focused on enhancing smart economy and 
smart mobility for example in Germany and less on enhancing smart people. Possibly citizens are already 
actively engaged through conventional means in land use planning and do not require smart big data to 
enhance or increase their involvement. Another explanation could be that it remains still difficult to use 

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spatial big data technologies and analytics for many unskilled people. This would suggest the need for more 
capacity development and the development of a sufficiently sized critical mass in this field. In both cases, the 
connection between geomatics, spatial planning and citizen sciences need to be improved and further 
developed. 
 

5. REFERENCES 

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https://doi.org/10.14710/geoplanning.5.2.205-214
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