New Solutions and Methodologies for Data Acquisition and Management in Small Municipalities

New Solutions and Methodologies for Data Acquisition and Management in Small Municipalities

Rui Pedro Juliao (Interdisciplinary Centre of Social Sciences (CICS.NOVA), NOVA FCSH, Universidade NOVA de Lisboa, Portugal), Amilton Amorim (UNESP, Brazil), João Paulo Hespanha (ESTGA, Universidade de Aveiro, Portugal), Guilherme Henrique Barros de Souza (UNESP, Brazil), Ronaldo Celso Messias Correia (UNESP, Brazil) and Rafael Delli Colli Destro (UNESP, Brazil)
DOI: 10.4018/978-1-7998-2249-3.ch004
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Promoting and managing sustainable territorial development require adequate tools that enable territorial managers to formulate appropriate choices. Technological solutions have emerged, and the paradigm has shifted from isolated GIS to a more collaborative production and dissemination of geographic data using spatial data infrastructures (SDI). A critical dataset for municipal land administration is cadastre. ISO 19152 standard of the Land Administration Domain Model (LADM) was published in 2012. Also, technology evolution, namely unmanned aerial vehicles (UAV), has changed data acquisition for cadastre. These are three pillars of modern territorial management: openness, co-production, and data sharing (SDI); models (LADM); affordable technology (UAV). This chapter presents how municipalities can develop an SDI project, incorporating LADM guidelines and UAV data acquisition. The case study is based on a group of 32 small municipalities from São Paulo state, in Brazil, known as UNIPONTAL.
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Territorial managers are facing huge challenges due to the increase of pace of spatial pressure and transformations. There are multiple and complex problems that require wise and informed decisions, and for that accurate spatial datasets and decision support models are mandatory. This is not an exclusive requirement of major urban and metropolitan areas, as in rural context economic agents do create expectations and put pressure on the governing organizations. Thus, promoting and managing sustainable spatial planning and territorial development of small size and income municipalities, namely in rural context, does also require adequate tools that enable decision makers to formulate appropriate choices.

During the last decades a large set of technological based solutions have been developed by the scientific community and business companies linking the ability to acquire, store, process and publish spatial datasets (basically, traditional GIS and Remote Sensing Applications) with the capability of doing it based on web services, taking advantage of Internet and the significant cost reduction of connectivity.

Therefore, today, more than the simple representation of land features through cartography, it is important to collect, organize, store, retrieve and explore spatial data finding the necessary knowledge for action. More than data repositories, it is important to have dynamic data flowing through the Information Society channels. In fact, the actual paradigm has shifted from an isolated GIS implementation approach to a more collaborative production and dissemination of Geographic Information, namely through the Internet, users groups and mobile technologies. It is an infrastructure approach, usually known by the designation of Spatial Data Infrastructures (SDI), which is shown in Figure 1.

Figure 1.

Paradigm change – From GIS to SDI

Source: Julião, 2010

Since SDI early days, and especially during the last years, there was a significant growing of interest about them acknowledging their important role in what concerns the formulation, implementation and monitoring of spatial public policies and private activities that spread over the territory. Technological growth and improvement, in particular the so-called Information and Communication Technologies (ICT), has helped in smart cities initiatives. Thus, Santana et al., 2016 pointed out four emerging technologies within systems for smart cities:

  • Internet of Things (IoT): It is the connection of everyday objects and sensors, transforming an entire environment into a network. Such objects may be lamps, refrigerators, microwaves, televisions, and other sensors and devices. For the operation of the network, each of the connected objects must be uniquely identiðed so that their access within the network is possible. Because large numbers of these objects are connected in a large urban center, smart cities systems are “forced” to use IoT-related technologies;

  • Big Data: Strongly related to IoT, as objects that structure a smart city network are sending information continuously, producing massive amounts of data;

  • Cloud Computing: Local physical storage has become a problem for complex smart city systems with a very high number of collected and processed data. Therefore, cloud computing can be a very useful technology for such systems because it offers a large, elastic and highly available infrastructure for both storage and processing of this data. Santana et al. (2016) also introduced the concept of “Cloud of Things,” which corresponds to the junction of IoT and Cloud Computing, where the second technology would process and store data from the first;

  • Participatory Mobile Sensing: In a smart city environment, citizens' own devices are used for data generation. Such devices may be, for example, smartphones and smartwatches, and data generated may be their location and travel speed. Areas such as health, transportation, safety and urban planning can take advantage of such data to improve their development. This is directly linked with the concept of Volunteered Geographic Information presented by Goodchild (2007), where he discussed the role of citizens becoming active environmental sensors.

Key Terms in this Chapter

Metadata: Data that describes the characteristics of a dataset or service.

Data Acquisition: Process of retrieving data regarding features of the real world to feed databases and information systems.

Viewer: Specific website or its component that enables the user to view and explore geographic datasets.

Infrastructure: Set of facilities that supports the provision of a service.

Cadastre: Spatial dataset regarding the localization, spatial configuration, and rights of a parcel

Web Services: Access to data or services between devices through the internet.

Catalogue: Specific website or its component that enables the user to search and evaluate existing geographic datasets or services through its metadata records.

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