## 2 Literature Review  
The proposed work falls under the category of contextual sensing. All physical objects (e.g., sensors, actuators, things) or human beings have some context attached to them in a given scenario. The context can be defined as \"any information that can be used to characterize the situation of an entity [12]. The context of an entity can be either external or internal; external contexts are perceived from the physical environment, such as sensing values from sensors or the location from GPS, whereas internal contexts are customized individual level contexts within a system [13]. In our proposed work, we focus on the external context as our scope is limited to geo-sensing services. One of the subcategories for applications of geo-sensors is the spatiotemporal contexts based services. The term spatiotemporal means belonging to both space and time; hence, the application of spatiotemporal is not just limited to the extraction and use of physical locations contexts [14; 15]. Many studies have been presented based on the applications of spatiotemporal with other attributes added to the given physical locations based on the specific time; such as effect of weather conditions at a given time at a location, such as rainfall on the mobility pattern in a city [16; 17], or the effect of environmental conditions, such as air quality on the traffic flow within a city [18]. The work presented in [19], discusses and analyzes the contextual sensing based on integrating contextual information with human and technical geo-sensor information for smart cities. The authors highlight the significance of contextual information and discuss the challenges regarding spatiotemporal contextual information. Three groups of sensors are discussed along with the three dimensions of sensing as data generation, geographic phenomena, and type of sensing. The authors aim to explore the use of geo-sensing capabilities and contextual information integration for the future development of smart cities. The sensors can be of various types, and, hence, their applications vary depending on the type of sensor and context of the sensor. Common terms used for different sub-types of sensors are mentioned as environmental sensors, mobile sensors, and pervasive sensors and people as sensors [19]. Multiple works based on sensing categories and contextual information has been presented. In environmental sensors, some contextual studies include flood monitoring [20; 21], air quality monitoring [22; 23; 24; 25; 26], and weather [27; 28]. In mobile sensors, some of the contextual studies presented include wearable ambient sensors applications [29; 30; 31; 32; 33; 34]. In pervasive sensing, some contextual studies include smart-aware environments/homes, assisted living [35; 36; 37; 38; 39; 40; 41; 42], healthcare [43; 44; 45]. In people as sensors, some of the contextual studies include flood monitoring [46; 47], sensing platforms for smart cities [48; 49; 50; 51; 52; 53; 54], healthcare [55; 56; 57; 58; 59]. Very few studies have presented and proposed the design and implementation of a GIS toolbox. The work presented in [60] proposes an adaptive GIS toolbox for hydrological modeling. In order to increase reusability and portability, the modules are programmed in an object-oriented fashion. The tasks of modeling elements of the hydrological cycle can be done using the toolbox, and it also supports different temporal and spatial scales. The presentation of the spatial, temporal, and geographical data is done using GRASS. The work presented in [61] proposes a landscape genetics GIS toolbox. It maps the genetic landscapes and summarized multiple genetic landscapes. Genetic diversity can be visualized using these tools. Together, these tools create genetic landscape surfaces directly from tables containing genetic distance or diversity data and sample location coordinates, greatly reducing the complexity of building and analyzing these raster surfaces in a Geographic Information System.